lec/lec04/lec04.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lecture 4 – Data 100, Fall 2022\n",
    "\n",
    "Will Fithian<br/>\n",
    "Content by Will Fithian, Lisa Yan, Joseph Gonzalez, Deborah Nolan, Sam Lau"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-02T15:15:15.363920Z",
     "start_time": "2018-02-02T15:15:14.337886Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "#%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (12, 9)\n",
    "\n",
    "sns.set()\n",
    "sns.set_context('talk')\n",
    "np.set_printoptions(threshold=20, precision=2, suppress=True)\n",
    "pd.set_option('display.max_rows', 30)\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.precision', 2)\n",
    "# This option stops scientific notation for pandas\n",
    "pd.set_option('display.float_format', '{:.2f}'.format)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Structure: File Formats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reproducible Data Science\n",
    "\n",
    "In the interest of **reproducible data science** we will download the data programatically.  We have defined some helper functions in the [ds100_utils.py](ds100_utils.py) file.  I can then reuse these helper functions in many different notebooks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-02T15:15:15.368501Z",
     "start_time": "2018-02-02T15:15:15.365808Z"
    }
   },
   "outputs": [],
   "source": [
    "from ds100_utils import fetch_and_cache"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "Occasionally, you will want to modify code that you have imported.  To reimport those modifications you can either use the python importlib library:\n",
    "\n",
    "```python\n",
    "from importlib import reload\n",
    "reload(utils)\n",
    "```\n",
    "\n",
    "or use iPython magic which will intelligently import code when files change:\n",
    "\n",
    "```python\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Downloading the Data\n",
    "\n",
    "Notice that because I record how I got the data in the notebook, others can reproduce this experiment.  However, it is worth noting that **the data can change**.  We will want to pay attention to file timestamps."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1) CSV: Restaurant data\n",
    "Restaurant food safety scores for restaurants in San Francisco. The scores and violation information have been made available by the [San Francisco Department of Public Health](https://data.sfgov.org/Health-and-Social-Services/Restaurant-Scores-LIVES-Standard/pyih-qa8i)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-02T15:15:16.391752Z",
     "start_time": "2018-02-02T15:15:15.413412Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using cached version that was downloaded (UTC): Tue Sep  6 02:37:41 2022\n"
     ]
    }
   ],
   "source": [
    "restaurants_file = fetch_and_cache(\n",
    "    \"https://data.sfgov.org/api/views/pyih-qa8i/rows.csv\", # DL from this website\n",
    "    \"restaurants.csv\",         # save as this file\n",
    "    force=False)               # do nothing if the file already exists"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we even begin to load the data it often helps to understand a little about the high-level structure:\n",
    "\n",
    "1. How big is the data file?\n",
    "1. How is the data file formatted?\n",
    "1. How do we read the data into pandas?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. How big is the data?\n",
    "\n",
    "I often like to start my analysis by getting a rough estimate of the size of the data.  This will help inform the tools I use and how I view the data.  If it is relatively small I might use a text editor or a spreadsheet to look at the data.  If it is larger, I might jump to more programmatic exploration or even used distributed computing tools.\n",
    "\n",
    "However here we will use python tools to probe the file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data/restaurants.csv is 12.093999 MB\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "print(restaurants_file, \"is\", os.path.getsize(restaurants_file) / 1e6, \"MB\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since these seem to be text files I might also want to investigate the number of lines, which often corresponds to the number of records."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-02T15:15:18.993584Z",
     "start_time": "2018-02-02T15:15:18.989848Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data/restaurants.csv is 53974 lines.\n"
     ]
    }
   ],
   "source": [
    "with open(restaurants_file, \"r\") as f:\n",
    "    print(restaurants_file, \"is\", sum(1 for l in f), \"lines.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 2. How is the data file formatted?\n",
    "\n",
    "Let's assume that these are text files (and do not contain binary encoded data) so we can print a \"few lines\" to get a better understanding of the file.\n",
    "\n",
    "Notice that below I use the `repr` function to return the raw string with special characters.  This is helpful in deducing the file format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data/restaurants.csv ======================\n",
      "000 | 'business_id,business_name,business_address,business_city,business_state,business_postal_code,business_latitude,business_longitude,business_location,business_phone_number,inspection_id,inspection_date,inspection_score,inspection_type,violation_id,violation_description,risk_category,Neighborhoods,SF Find Neighborhoods,Current Police Districts,Current Supervisor Districts,Analysis Neighborhoods\\n'\n",
      "001 | '835,Kam Po Kitchen,801 Broadway St,San Francisco,CA,94133,37.797223,-122.410513,POINT (-122.410513 37.797223),,835_20180917,09/17/2018 12:00:00 AM,88,Routine - Unscheduled,835_20180917_103139,Improper food storage,Low Risk,107,107,6,3,6\\n'\n",
      "002 | \"905,Working Girls' Cafe',0259 Kearny St,San Francisco,CA,94108,37.790477,-122.404033,POINT (-122.404033 37.790477),,905_20190415,04/15/2019 12:00:00 AM,87,Routine - Unscheduled,905_20190415_103114,High risk vermin infestation,High Risk,19,19,6,3,8\\n\"\n",
      "003 | \"1203,TAWAN'S THAI FOOD,4403 GEARY Blvd,San Francisco,CA,94118,37.780834,-122.46659,POINT (-122.46659 37.780834),+14155755175,1203_20170803,08/03/2017 12:00:00 AM,77,Routine - Unscheduled,1203_20170803_103120,Moderate risk food holding temperature,Moderate Risk,5,5,8,4,11\\n\"\n",
      "004 | '1345,Cordon Bleu,1574 California St,San Francisco,CA,94109,37.790683,-122.420264,POINT (-122.420264 37.790683),,1345_20170928,09/28/2017 12:00:00 AM,81,Routine - Unscheduled,1345_20170928_103105,Improper cooling methods,High Risk,105,105,4,3,21\\n'\n",
      "005 | '1352,LA TORTILLA,495 Castro St B,San Francisco,CA,94114,37.760954,-122.434935,POINT (-122.434935 37.760954),+14155863990,1352_20180620,06/20/2018 12:00:00 AM,74,Routine - Unscheduled,1352_20180620_103177,Non service animal,Low Risk,38,38,3,5,5\\n'\n",
      "006 | '1652,STARBUCKS,201 03rd St,San Francisco,CA,94105,37.785037,-122.400432,POINT (-122.400432 37.785037),,1652_20190611,06/11/2019 12:00:00 AM,96,Routine - Unscheduled,1652_20190611_103161,Low risk vermin infestation,Low Risk,32,32,1,10,8\\n'\n",
      "007 | '1922,All stars Donuts,901 Clement St,San Francisco,CA,94118,37.782694,-122.468969,POINT (-122.468969 37.782694),,1922_20161128,11/28/2016 12:00:00 AM,,Complaint,1922_20161128_103139,Improper food storage,Low Risk,5,5,8,4,11\\n'\n",
      "008 | \"3019,Tad's,120 Powell St,San Francisco,CA,94102,37.785928,-122.407887,POINT (-122.407887 37.785928),,3019_20190702,07/02/2019 12:00:00 AM,88,Routine - Unscheduled,3019_20190702_103139,Improper food storage,Low Risk,19,19,6,3,8\\n\"\n",
      "009 | '3286,Super Burrito,1200 Polk St,San Francisco,CA,94109,37.787998,-122.420117,POINT (-122.420117 37.787998),,3286_20190214,02/14/2019 12:00:00 AM,81,Routine - Unscheduled,3286_20190214_103105,Improper cooling methods,High Risk,50,50,4,3,21\\n'\n"
     ]
    }
   ],
   "source": [
    "print(restaurants_file, \"======================\")\n",
    "with open(restaurants_file, \"r\") as f:\n",
    "    for i in range(10):\n",
    "        print(f\"{i:03} | {repr(f.readline())}\") # zero pad line numbers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. How do we read the data into pandas?\n",
    "\n",
    "With CSVs, we can use the handy `pd.read_csv` [function](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html?highlight=read_csv):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>business_id</th>\n",
       "      <th>business_name</th>\n",
       "      <th>business_address</th>\n",
       "      <th>business_city</th>\n",
       "      <th>business_state</th>\n",
       "      <th>business_postal_code</th>\n",
       "      <th>business_latitude</th>\n",
       "      <th>business_longitude</th>\n",
       "      <th>business_location</th>\n",
       "      <th>business_phone_number</th>\n",
       "      <th>inspection_id</th>\n",
       "      <th>inspection_date</th>\n",
       "      <th>inspection_score</th>\n",
       "      <th>inspection_type</th>\n",
       "      <th>violation_id</th>\n",
       "      <th>violation_description</th>\n",
       "      <th>risk_category</th>\n",
       "      <th>Neighborhoods</th>\n",
       "      <th>SF Find Neighborhoods</th>\n",
       "      <th>Current Police Districts</th>\n",
       "      <th>Current Supervisor Districts</th>\n",
       "      <th>Analysis Neighborhoods</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>835</td>\n",
       "      <td>Kam Po Kitchen</td>\n",
       "      <td>801 Broadway St</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94133</td>\n",
       "      <td>37.80</td>\n",
       "      <td>-122.41</td>\n",
       "      <td>POINT (-122.410513 37.797223)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>835_20180917</td>\n",
       "      <td>09/17/2018 12:00:00 AM</td>\n",
       "      <td>88.00</td>\n",
       "      <td>Routine - Unscheduled</td>\n",
       "      <td>835_20180917_103139</td>\n",
       "      <td>Improper food storage</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>107.00</td>\n",
       "      <td>107.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>905</td>\n",
       "      <td>Working Girls' Cafe'</td>\n",
       "      <td>0259 Kearny St</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94108</td>\n",
       "      <td>37.79</td>\n",
       "      <td>-122.40</td>\n",
       "      <td>POINT (-122.404033 37.790477)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>905_20190415</td>\n",
       "      <td>04/15/2019 12:00:00 AM</td>\n",
       "      <td>87.00</td>\n",
       "      <td>Routine - Unscheduled</td>\n",
       "      <td>905_20190415_103114</td>\n",
       "      <td>High risk vermin infestation</td>\n",
       "      <td>High Risk</td>\n",
       "      <td>19.00</td>\n",
       "      <td>19.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1203</td>\n",
       "      <td>TAWAN'S THAI FOOD</td>\n",
       "      <td>4403 GEARY Blvd</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94118</td>\n",
       "      <td>37.78</td>\n",
       "      <td>-122.47</td>\n",
       "      <td>POINT (-122.46659 37.780834)</td>\n",
       "      <td>14155755175.00</td>\n",
       "      <td>1203_20170803</td>\n",
       "      <td>08/03/2017 12:00:00 AM</td>\n",
       "      <td>77.00</td>\n",
       "      <td>Routine - Unscheduled</td>\n",
       "      <td>1203_20170803_103120</td>\n",
       "      <td>Moderate risk food holding temperature</td>\n",
       "      <td>Moderate Risk</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>11.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1345</td>\n",
       "      <td>Cordon Bleu</td>\n",
       "      <td>1574 California St</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94109</td>\n",
       "      <td>37.79</td>\n",
       "      <td>-122.42</td>\n",
       "      <td>POINT (-122.420264 37.790683)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1345_20170928</td>\n",
       "      <td>09/28/2017 12:00:00 AM</td>\n",
       "      <td>81.00</td>\n",
       "      <td>Routine - Unscheduled</td>\n",
       "      <td>1345_20170928_103105</td>\n",
       "      <td>Improper cooling methods</td>\n",
       "      <td>High Risk</td>\n",
       "      <td>105.00</td>\n",
       "      <td>105.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>21.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1352</td>\n",
       "      <td>LA TORTILLA</td>\n",
       "      <td>495 Castro St B</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94114</td>\n",
       "      <td>37.76</td>\n",
       "      <td>-122.43</td>\n",
       "      <td>POINT (-122.434935 37.760954)</td>\n",
       "      <td>14155863990.00</td>\n",
       "      <td>1352_20180620</td>\n",
       "      <td>06/20/2018 12:00:00 AM</td>\n",
       "      <td>74.00</td>\n",
       "      <td>Routine - Unscheduled</td>\n",
       "      <td>1352_20180620_103177</td>\n",
       "      <td>Non service animal</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>38.00</td>\n",
       "      <td>38.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   business_id         business_name    business_address  business_city  \\\n",
       "0          835        Kam Po Kitchen     801 Broadway St  San Francisco   \n",
       "1          905  Working Girls' Cafe'      0259 Kearny St  San Francisco   \n",
       "2         1203     TAWAN'S THAI FOOD     4403 GEARY Blvd  San Francisco   \n",
       "3         1345           Cordon Bleu  1574 California St  San Francisco   \n",
       "4         1352           LA TORTILLA     495 Castro St B  San Francisco   \n",
       "\n",
       "  business_state business_postal_code  business_latitude  business_longitude  \\\n",
       "0             CA                94133              37.80             -122.41   \n",
       "1             CA                94108              37.79             -122.40   \n",
       "2             CA                94118              37.78             -122.47   \n",
       "3             CA                94109              37.79             -122.42   \n",
       "4             CA                94114              37.76             -122.43   \n",
       "\n",
       "               business_location  business_phone_number  inspection_id  \\\n",
       "0  POINT (-122.410513 37.797223)                    NaN   835_20180917   \n",
       "1  POINT (-122.404033 37.790477)                    NaN   905_20190415   \n",
       "2   POINT (-122.46659 37.780834)         14155755175.00  1203_20170803   \n",
       "3  POINT (-122.420264 37.790683)                    NaN  1345_20170928   \n",
       "4  POINT (-122.434935 37.760954)         14155863990.00  1352_20180620   \n",
       "\n",
       "          inspection_date  inspection_score        inspection_type  \\\n",
       "0  09/17/2018 12:00:00 AM             88.00  Routine - Unscheduled   \n",
       "1  04/15/2019 12:00:00 AM             87.00  Routine - Unscheduled   \n",
       "2  08/03/2017 12:00:00 AM             77.00  Routine - Unscheduled   \n",
       "3  09/28/2017 12:00:00 AM             81.00  Routine - Unscheduled   \n",
       "4  06/20/2018 12:00:00 AM             74.00  Routine - Unscheduled   \n",
       "\n",
       "           violation_id                   violation_description  \\\n",
       "0   835_20180917_103139                   Improper food storage   \n",
       "1   905_20190415_103114            High risk vermin infestation   \n",
       "2  1203_20170803_103120  Moderate risk food holding temperature   \n",
       "3  1345_20170928_103105                Improper cooling methods   \n",
       "4  1352_20180620_103177                      Non service animal   \n",
       "\n",
       "   risk_category  Neighborhoods  SF Find Neighborhoods  \\\n",
       "0       Low Risk         107.00                 107.00   \n",
       "1      High Risk          19.00                  19.00   \n",
       "2  Moderate Risk           5.00                   5.00   \n",
       "3      High Risk         105.00                 105.00   \n",
       "4       Low Risk          38.00                  38.00   \n",
       "\n",
       "   Current Police Districts  Current Supervisor Districts  \\\n",
       "0                      6.00                          3.00   \n",
       "1                      6.00                          3.00   \n",
       "2                      8.00                          4.00   \n",
       "3                      4.00                          3.00   \n",
       "4                      3.00                          5.00   \n",
       "\n",
       "   Analysis Neighborhoods  \n",
       "0                    6.00  \n",
       "1                    8.00  \n",
       "2                   11.00  \n",
       "3                   21.00  \n",
       "4                    5.00  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "restaurants = pd.read_csv(restaurants_file)\n",
    "restaurants.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Side Note: TSV\n",
    "\n",
    "We won't go through all the steps for TSV, but here's how the same SF restaurants data would look like in a TSV:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data/restaurants.tsv ======================\n",
      "000 | '\\tbusiness_id\\tbusiness_name\\tbusiness_address\\tbusiness_city\\tbusiness_state\\tbusiness_postal_code\\tbusiness_latitude\\tbusiness_longitude\\tbusiness_location\\tbusiness_phone_number\\tinspection_id\\tinspection_date\\tinspection_score\\tinspection_type\\tviolation_id\\tviolation_description\\trisk_category\\tNeighborhoods\\tSF Find Neighborhoods\\tCurrent Police Districts\\tCurrent Supervisor Districts\\tAnalysis Neighborhoods\\n'\n",
      "001 | '0\\t835\\tKam Po Kitchen\\t801 Broadway St\\tSan Francisco\\tCA\\t94133\\t37.797223\\t-122.410513\\tPOINT (-122.410513 37.797223)\\t\\t835_20180917\\t09/17/2018 12:00:00 AM\\t88.0\\tRoutine - Unscheduled\\t835_20180917_103139\\tImproper food storage\\tLow Risk\\t107.0\\t107.0\\t6.0\\t3.0\\t6.0\\n'\n",
      "002 | \"1\\t905\\tWorking Girls' Cafe'\\t0259 Kearny St\\tSan Francisco\\tCA\\t94108\\t37.790477\\t-122.404033\\tPOINT (-122.404033 37.790477)\\t\\t905_20190415\\t04/15/2019 12:00:00 AM\\t87.0\\tRoutine - Unscheduled\\t905_20190415_103114\\tHigh risk vermin infestation\\tHigh Risk\\t19.0\\t19.0\\t6.0\\t3.0\\t8.0\\n\"\n",
      "003 | \"2\\t1203\\tTAWAN'S THAI FOOD\\t4403 GEARY Blvd\\tSan Francisco\\tCA\\t94118\\t37.780834\\t-122.46659\\tPOINT (-122.46659 37.780834)\\t14155755175.0\\t1203_20170803\\t08/03/2017 12:00:00 AM\\t77.0\\tRoutine - Unscheduled\\t1203_20170803_103120\\tModerate risk food holding temperature\\tModerate Risk\\t5.0\\t5.0\\t8.0\\t4.0\\t11.0\\n\"\n",
      "004 | '3\\t1345\\tCordon Bleu\\t1574 California St\\tSan Francisco\\tCA\\t94109\\t37.790683\\t-122.420264\\tPOINT (-122.420264 37.790683)\\t\\t1345_20170928\\t09/28/2017 12:00:00 AM\\t81.0\\tRoutine - Unscheduled\\t1345_20170928_103105\\tImproper cooling methods\\tHigh Risk\\t105.0\\t105.0\\t4.0\\t3.0\\t21.0\\n'\n",
      "005 | '4\\t1352\\tLA TORTILLA\\t495 Castro St B\\tSan Francisco\\tCA\\t94114\\t37.760954\\t-122.434935\\tPOINT (-122.434935 37.760954)\\t14155863990.0\\t1352_20180620\\t06/20/2018 12:00:00 AM\\t74.0\\tRoutine - Unscheduled\\t1352_20180620_103177\\tNon service animal\\tLow Risk\\t38.0\\t38.0\\t3.0\\t5.0\\t5.0\\n'\n",
      "006 | '5\\t1652\\tSTARBUCKS\\t201 03rd St\\tSan Francisco\\tCA\\t94105\\t37.785037\\t-122.400432\\tPOINT (-122.400432 37.785037)\\t\\t1652_20190611\\t06/11/2019 12:00:00 AM\\t96.0\\tRoutine - Unscheduled\\t1652_20190611_103161\\tLow risk vermin infestation\\tLow Risk\\t32.0\\t32.0\\t1.0\\t10.0\\t8.0\\n'\n",
      "007 | '6\\t1922\\tAll stars Donuts\\t901 Clement St\\tSan Francisco\\tCA\\t94118\\t37.782694\\t-122.468969\\tPOINT (-122.468969 37.782694)\\t\\t1922_20161128\\t11/28/2016 12:00:00 AM\\t\\tComplaint\\t1922_20161128_103139\\tImproper food storage\\tLow Risk\\t5.0\\t5.0\\t8.0\\t4.0\\t11.0\\n'\n",
      "008 | \"7\\t3019\\tTad's\\t120 Powell St\\tSan Francisco\\tCA\\t94102\\t37.785928\\t-122.407887\\tPOINT (-122.407887 37.785928)\\t\\t3019_20190702\\t07/02/2019 12:00:00 AM\\t88.0\\tRoutine - Unscheduled\\t3019_20190702_103139\\tImproper food storage\\tLow Risk\\t19.0\\t19.0\\t6.0\\t3.0\\t8.0\\n\"\n",
      "009 | '8\\t3286\\tSuper Burrito\\t1200 Polk St\\tSan Francisco\\tCA\\t94109\\t37.787998\\t-122.420117\\tPOINT (-122.420117 37.787998)\\t\\t3286_20190214\\t02/14/2019 12:00:00 AM\\t81.0\\tRoutine - Unscheduled\\t3286_20190214_103105\\tImproper cooling methods\\tHigh Risk\\t50.0\\t50.0\\t4.0\\t3.0\\t21.0\\n'\n"
     ]
    }
   ],
   "source": [
    "restaurants_tsv_file = \"data/restaurants.tsv\"  # I processed this earlier\n",
    "\n",
    "print(restaurants_tsv_file, \"======================\")\n",
    "with open(restaurants_tsv_file, \"r\") as f:\n",
    "    for i in range(10):\n",
    "        print(f\"{i:03} | {repr(f.readline())}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `pd.read_csv` function also reads in TSVs if we specify the `'\\t'` delimiter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>business_id</th>\n",
       "      <th>business_name</th>\n",
       "      <th>business_address</th>\n",
       "      <th>business_city</th>\n",
       "      <th>business_state</th>\n",
       "      <th>business_postal_code</th>\n",
       "      <th>business_latitude</th>\n",
       "      <th>business_longitude</th>\n",
       "      <th>business_location</th>\n",
       "      <th>business_phone_number</th>\n",
       "      <th>inspection_id</th>\n",
       "      <th>inspection_date</th>\n",
       "      <th>inspection_score</th>\n",
       "      <th>inspection_type</th>\n",
       "      <th>violation_id</th>\n",
       "      <th>violation_description</th>\n",
       "      <th>risk_category</th>\n",
       "      <th>Neighborhoods</th>\n",
       "      <th>SF Find Neighborhoods</th>\n",
       "      <th>Current Police Districts</th>\n",
       "      <th>Current Supervisor Districts</th>\n",
       "      <th>Analysis Neighborhoods</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>835</td>\n",
       "      <td>Kam Po Kitchen</td>\n",
       "      <td>801 Broadway St</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94133</td>\n",
       "      <td>37.80</td>\n",
       "      <td>-122.41</td>\n",
       "      <td>POINT (-122.410513 37.797223)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>835_20180917</td>\n",
       "      <td>09/17/2018 12:00:00 AM</td>\n",
       "      <td>88.00</td>\n",
       "      <td>Routine - Unscheduled</td>\n",
       "      <td>835_20180917_103139</td>\n",
       "      <td>Improper food storage</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>107.00</td>\n",
       "      <td>107.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>905</td>\n",
       "      <td>Working Girls' Cafe'</td>\n",
       "      <td>0259 Kearny St</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94108</td>\n",
       "      <td>37.79</td>\n",
       "      <td>-122.40</td>\n",
       "      <td>POINT (-122.404033 37.790477)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>905_20190415</td>\n",
       "      <td>04/15/2019 12:00:00 AM</td>\n",
       "      <td>87.00</td>\n",
       "      <td>Routine - Unscheduled</td>\n",
       "      <td>905_20190415_103114</td>\n",
       "      <td>High risk vermin infestation</td>\n",
       "      <td>High Risk</td>\n",
       "      <td>19.00</td>\n",
       "      <td>19.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1203</td>\n",
       "      <td>TAWAN'S THAI FOOD</td>\n",
       "      <td>4403 GEARY Blvd</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94118</td>\n",
       "      <td>37.78</td>\n",
       "      <td>-122.47</td>\n",
       "      <td>POINT (-122.46659 37.780834)</td>\n",
       "      <td>14155755175.00</td>\n",
       "      <td>1203_20170803</td>\n",
       "      <td>08/03/2017 12:00:00 AM</td>\n",
       "      <td>77.00</td>\n",
       "      <td>Routine - Unscheduled</td>\n",
       "      <td>1203_20170803_103120</td>\n",
       "      <td>Moderate risk food holding temperature</td>\n",
       "      <td>Moderate Risk</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>11.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1345</td>\n",
       "      <td>Cordon Bleu</td>\n",
       "      <td>1574 California St</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94109</td>\n",
       "      <td>37.79</td>\n",
       "      <td>-122.42</td>\n",
       "      <td>POINT (-122.420264 37.790683)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1345_20170928</td>\n",
       "      <td>09/28/2017 12:00:00 AM</td>\n",
       "      <td>81.00</td>\n",
       "      <td>Routine - Unscheduled</td>\n",
       "      <td>1345_20170928_103105</td>\n",
       "      <td>Improper cooling methods</td>\n",
       "      <td>High Risk</td>\n",
       "      <td>105.00</td>\n",
       "      <td>105.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>21.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1352</td>\n",
       "      <td>LA TORTILLA</td>\n",
       "      <td>495 Castro St B</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94114</td>\n",
       "      <td>37.76</td>\n",
       "      <td>-122.43</td>\n",
       "      <td>POINT (-122.434935 37.760954)</td>\n",
       "      <td>14155863990.00</td>\n",
       "      <td>1352_20180620</td>\n",
       "      <td>06/20/2018 12:00:00 AM</td>\n",
       "      <td>74.00</td>\n",
       "      <td>Routine - Unscheduled</td>\n",
       "      <td>1352_20180620_103177</td>\n",
       "      <td>Non service animal</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>38.00</td>\n",
       "      <td>38.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  business_id         business_name    business_address  \\\n",
       "0           0          835        Kam Po Kitchen     801 Broadway St   \n",
       "1           1          905  Working Girls' Cafe'      0259 Kearny St   \n",
       "2           2         1203     TAWAN'S THAI FOOD     4403 GEARY Blvd   \n",
       "3           3         1345           Cordon Bleu  1574 California St   \n",
       "4           4         1352           LA TORTILLA     495 Castro St B   \n",
       "\n",
       "   business_city business_state business_postal_code  business_latitude  \\\n",
       "0  San Francisco             CA                94133              37.80   \n",
       "1  San Francisco             CA                94108              37.79   \n",
       "2  San Francisco             CA                94118              37.78   \n",
       "3  San Francisco             CA                94109              37.79   \n",
       "4  San Francisco             CA                94114              37.76   \n",
       "\n",
       "   business_longitude              business_location  business_phone_number  \\\n",
       "0             -122.41  POINT (-122.410513 37.797223)                    NaN   \n",
       "1             -122.40  POINT (-122.404033 37.790477)                    NaN   \n",
       "2             -122.47   POINT (-122.46659 37.780834)         14155755175.00   \n",
       "3             -122.42  POINT (-122.420264 37.790683)                    NaN   \n",
       "4             -122.43  POINT (-122.434935 37.760954)         14155863990.00   \n",
       "\n",
       "   inspection_id         inspection_date  inspection_score  \\\n",
       "0   835_20180917  09/17/2018 12:00:00 AM             88.00   \n",
       "1   905_20190415  04/15/2019 12:00:00 AM             87.00   \n",
       "2  1203_20170803  08/03/2017 12:00:00 AM             77.00   \n",
       "3  1345_20170928  09/28/2017 12:00:00 AM             81.00   \n",
       "4  1352_20180620  06/20/2018 12:00:00 AM             74.00   \n",
       "\n",
       "         inspection_type          violation_id  \\\n",
       "0  Routine - Unscheduled   835_20180917_103139   \n",
       "1  Routine - Unscheduled   905_20190415_103114   \n",
       "2  Routine - Unscheduled  1203_20170803_103120   \n",
       "3  Routine - Unscheduled  1345_20170928_103105   \n",
       "4  Routine - Unscheduled  1352_20180620_103177   \n",
       "\n",
       "                    violation_description  risk_category  Neighborhoods  \\\n",
       "0                   Improper food storage       Low Risk         107.00   \n",
       "1            High risk vermin infestation      High Risk          19.00   \n",
       "2  Moderate risk food holding temperature  Moderate Risk           5.00   \n",
       "3                Improper cooling methods      High Risk         105.00   \n",
       "4                      Non service animal       Low Risk          38.00   \n",
       "\n",
       "   SF Find Neighborhoods  Current Police Districts  \\\n",
       "0                 107.00                      6.00   \n",
       "1                  19.00                      6.00   \n",
       "2                   5.00                      8.00   \n",
       "3                 105.00                      4.00   \n",
       "4                  38.00                      3.00   \n",
       "\n",
       "   Current Supervisor Districts  Analysis Neighborhoods  \n",
       "0                          3.00                    6.00  \n",
       "1                          3.00                    8.00  \n",
       "2                          4.00                   11.00  \n",
       "3                          3.00                   21.00  \n",
       "4                          5.00                    5.00  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "restaurants_tsv = pd.read_csv(restaurants_tsv_file, delimiter='\\t')\n",
    "restaurants_tsv.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br/>\n",
    "Note how records with special punctuation (e.g., a comma or apostrophe) are stored in the text file, and how `pd.read_csv` parses them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "062 | '81508,\"Mo\\'z Cafe, Inc.\",36 05th St,San Francisco,CA,94103,,,,,81508_20190516,05/16/2019 12:00:00 AM,84,Routine - Unscheduled,81508_20190516_103162,Other low risk violation,Low Risk,,,,,\\n'\n"
     ]
    }
   ],
   "source": [
    "# punctuation in a CSV record entry\n",
    "with open(restaurants_file, \"r\") as f:\n",
    "    for i, line in enumerate(f.readlines()):\n",
    "        if \"Mo'z Cafe,\" in line:\n",
    "            print(f\"{i:03} | {repr(line)}\")\n",
    "            break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>business_id</th>\n",
       "      <th>business_name</th>\n",
       "      <th>business_address</th>\n",
       "      <th>business_city</th>\n",
       "      <th>business_state</th>\n",
       "      <th>business_postal_code</th>\n",
       "      <th>business_latitude</th>\n",
       "      <th>business_longitude</th>\n",
       "      <th>business_location</th>\n",
       "      <th>business_phone_number</th>\n",
       "      <th>inspection_id</th>\n",
       "      <th>inspection_date</th>\n",
       "      <th>inspection_score</th>\n",
       "      <th>inspection_type</th>\n",
       "      <th>violation_id</th>\n",
       "      <th>violation_description</th>\n",
       "      <th>risk_category</th>\n",
       "      <th>Neighborhoods</th>\n",
       "      <th>SF Find Neighborhoods</th>\n",
       "      <th>Current Police Districts</th>\n",
       "      <th>Current Supervisor Districts</th>\n",
       "      <th>Analysis Neighborhoods</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>81508</td>\n",
       "      <td>Mo'z Cafe, Inc.</td>\n",
       "      <td>36 05th St</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94103</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>81508_20190516</td>\n",
       "      <td>05/16/2019 12:00:00 AM</td>\n",
       "      <td>84.00</td>\n",
       "      <td>Routine - Unscheduled</td>\n",
       "      <td>81508_20190516_103162</td>\n",
       "      <td>Other low risk violation</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18913</th>\n",
       "      <td>91619</td>\n",
       "      <td>Mo'z Cafe S.F. Inc</td>\n",
       "      <td>5 Masonic Ave</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94118</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>91619_20190410</td>\n",
       "      <td>04/10/2019 12:00:00 AM</td>\n",
       "      <td>72.00</td>\n",
       "      <td>Routine - Unscheduled</td>\n",
       "      <td>91619_20190410_103119</td>\n",
       "      <td>Inadequate and inaccessible handwashing facili...</td>\n",
       "      <td>Moderate Risk</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20034</th>\n",
       "      <td>91619</td>\n",
       "      <td>Mo'z Cafe S.F. Inc</td>\n",
       "      <td>5 Masonic Ave</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94118</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>91619_20180716</td>\n",
       "      <td>07/16/2018 12:00:00 AM</td>\n",
       "      <td>83.00</td>\n",
       "      <td>Routine - Unscheduled</td>\n",
       "      <td>91619_20180716_103109</td>\n",
       "      <td>Unclean or unsanitary food contact surfaces</td>\n",
       "      <td>High Risk</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22508</th>\n",
       "      <td>91619</td>\n",
       "      <td>Mo'z Cafe S.F. Inc</td>\n",
       "      <td>5 Masonic Ave</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94118</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>91619_20190410</td>\n",
       "      <td>04/10/2019 12:00:00 AM</td>\n",
       "      <td>72.00</td>\n",
       "      <td>Routine - Unscheduled</td>\n",
       "      <td>91619_20190410_103144</td>\n",
       "      <td>Unapproved or unmaintained equipment or utensils</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23468</th>\n",
       "      <td>81508</td>\n",
       "      <td>Mo'z Cafe, Inc.</td>\n",
       "      <td>36 05th St</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94103</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>81508_20170927</td>\n",
       "      <td>09/27/2017 12:00:00 AM</td>\n",
       "      <td>80.00</td>\n",
       "      <td>Routine - Unscheduled</td>\n",
       "      <td>81508_20170927_103103</td>\n",
       "      <td>High risk food holding temperature</td>\n",
       "      <td>High Risk</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       business_id       business_name business_address  business_city  \\\n",
       "61           81508     Mo'z Cafe, Inc.       36 05th St  San Francisco   \n",
       "18913        91619  Mo'z Cafe S.F. Inc    5 Masonic Ave  San Francisco   \n",
       "20034        91619  Mo'z Cafe S.F. Inc    5 Masonic Ave  San Francisco   \n",
       "22508        91619  Mo'z Cafe S.F. Inc    5 Masonic Ave  San Francisco   \n",
       "23468        81508     Mo'z Cafe, Inc.       36 05th St  San Francisco   \n",
       "\n",
       "      business_state business_postal_code  business_latitude  \\\n",
       "61                CA                94103                NaN   \n",
       "18913             CA                94118                NaN   \n",
       "20034             CA                94118                NaN   \n",
       "22508             CA                94118                NaN   \n",
       "23468             CA                94103                NaN   \n",
       "\n",
       "       business_longitude business_location  business_phone_number  \\\n",
       "61                    NaN               NaN                    NaN   \n",
       "18913                 NaN               NaN                    NaN   \n",
       "20034                 NaN               NaN                    NaN   \n",
       "22508                 NaN               NaN                    NaN   \n",
       "23468                 NaN               NaN                    NaN   \n",
       "\n",
       "        inspection_id         inspection_date  inspection_score  \\\n",
       "61     81508_20190516  05/16/2019 12:00:00 AM             84.00   \n",
       "18913  91619_20190410  04/10/2019 12:00:00 AM             72.00   \n",
       "20034  91619_20180716  07/16/2018 12:00:00 AM             83.00   \n",
       "22508  91619_20190410  04/10/2019 12:00:00 AM             72.00   \n",
       "23468  81508_20170927  09/27/2017 12:00:00 AM             80.00   \n",
       "\n",
       "             inspection_type           violation_id  \\\n",
       "61     Routine - Unscheduled  81508_20190516_103162   \n",
       "18913  Routine - Unscheduled  91619_20190410_103119   \n",
       "20034  Routine - Unscheduled  91619_20180716_103109   \n",
       "22508  Routine - Unscheduled  91619_20190410_103144   \n",
       "23468  Routine - Unscheduled  81508_20170927_103103   \n",
       "\n",
       "                                   violation_description  risk_category  \\\n",
       "61                              Other low risk violation       Low Risk   \n",
       "18913  Inadequate and inaccessible handwashing facili...  Moderate Risk   \n",
       "20034        Unclean or unsanitary food contact surfaces      High Risk   \n",
       "22508   Unapproved or unmaintained equipment or utensils       Low Risk   \n",
       "23468                 High risk food holding temperature      High Risk   \n",
       "\n",
       "       Neighborhoods  SF Find Neighborhoods  Current Police Districts  \\\n",
       "61               NaN                    NaN                       NaN   \n",
       "18913            NaN                    NaN                       NaN   \n",
       "20034            NaN                    NaN                       NaN   \n",
       "22508            NaN                    NaN                       NaN   \n",
       "23468            NaN                    NaN                       NaN   \n",
       "\n",
       "       Current Supervisor Districts  Analysis Neighborhoods  \n",
       "61                              NaN                     NaN  \n",
       "18913                           NaN                     NaN  \n",
       "20034                           NaN                     NaN  \n",
       "22508                           NaN                     NaN  \n",
       "23468                           NaN                     NaN  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read_csv is smart!\n",
    "restaurants[restaurants['business_name'].str.contains(\"Mo'z\")].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n",
    "### 4. What's in the data set?\n",
    "\n",
    "You will explore this data set more in HW 2, but for now let's see what is in there\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>business_id</th>\n",
       "      <th>business_latitude</th>\n",
       "      <th>business_longitude</th>\n",
       "      <th>business_phone_number</th>\n",
       "      <th>inspection_score</th>\n",
       "      <th>Neighborhoods</th>\n",
       "      <th>SF Find Neighborhoods</th>\n",
       "      <th>Current Police Districts</th>\n",
       "      <th>Current Supervisor Districts</th>\n",
       "      <th>Analysis Neighborhoods</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>53973.00</td>\n",
       "      <td>27475.00</td>\n",
       "      <td>27475.00</td>\n",
       "      <td>17672.00</td>\n",
       "      <td>39541.00</td>\n",
       "      <td>27435.00</td>\n",
       "      <td>27435.00</td>\n",
       "      <td>27447.00</td>\n",
       "      <td>27447.00</td>\n",
       "      <td>27447.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>55218.88</td>\n",
       "      <td>37.75</td>\n",
       "      <td>-122.36</td>\n",
       "      <td>14155413305.28</td>\n",
       "      <td>86.22</td>\n",
       "      <td>56.96</td>\n",
       "      <td>56.96</td>\n",
       "      <td>5.36</td>\n",
       "      <td>5.63</td>\n",
       "      <td>19.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>36165.56</td>\n",
       "      <td>0.88</td>\n",
       "      <td>2.86</td>\n",
       "      <td>1356437.67</td>\n",
       "      <td>8.49</td>\n",
       "      <td>34.80</td>\n",
       "      <td>34.80</td>\n",
       "      <td>2.69</td>\n",
       "      <td>3.30</td>\n",
       "      <td>11.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>19.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-122.51</td>\n",
       "      <td>14150204876.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7558.00</td>\n",
       "      <td>37.76</td>\n",
       "      <td>-122.44</td>\n",
       "      <td>14155371185.00</td>\n",
       "      <td>81.00</td>\n",
       "      <td>28.00</td>\n",
       "      <td>28.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>69186.00</td>\n",
       "      <td>37.78</td>\n",
       "      <td>-122.42</td>\n",
       "      <td>14155608877.00</td>\n",
       "      <td>87.00</td>\n",
       "      <td>53.00</td>\n",
       "      <td>53.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>20.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>86385.00</td>\n",
       "      <td>37.79</td>\n",
       "      <td>-122.41</td>\n",
       "      <td>14155802771.00</td>\n",
       "      <td>92.00</td>\n",
       "      <td>95.00</td>\n",
       "      <td>95.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>30.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>102705.00</td>\n",
       "      <td>37.82</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14159881393.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>117.00</td>\n",
       "      <td>117.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>41.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       business_id  business_latitude  business_longitude  \\\n",
       "count     53973.00           27475.00            27475.00   \n",
       "mean      55218.88              37.75             -122.36   \n",
       "std       36165.56               0.88                2.86   \n",
       "min          19.00               0.00             -122.51   \n",
       "25%        7558.00              37.76             -122.44   \n",
       "50%       69186.00              37.78             -122.42   \n",
       "75%       86385.00              37.79             -122.41   \n",
       "max      102705.00              37.82                0.00   \n",
       "\n",
       "       business_phone_number  inspection_score  Neighborhoods  \\\n",
       "count               17672.00          39541.00       27435.00   \n",
       "mean          14155413305.28             86.22          56.96   \n",
       "std               1356437.67              8.49          34.80   \n",
       "min           14150204876.00             45.00           1.00   \n",
       "25%           14155371185.00             81.00          28.00   \n",
       "50%           14155608877.00             87.00          53.00   \n",
       "75%           14155802771.00             92.00          95.00   \n",
       "max           14159881393.00            100.00         117.00   \n",
       "\n",
       "       SF Find Neighborhoods  Current Police Districts  \\\n",
       "count               27435.00                  27447.00   \n",
       "mean                   56.96                      5.36   \n",
       "std                    34.80                      2.69   \n",
       "min                     1.00                      1.00   \n",
       "25%                    28.00                      3.00   \n",
       "50%                    53.00                      6.00   \n",
       "75%                    95.00                      8.00   \n",
       "max                   117.00                     10.00   \n",
       "\n",
       "       Current Supervisor Districts  Analysis Neighborhoods  \n",
       "count                      27447.00                27447.00  \n",
       "mean                           5.63                   19.46  \n",
       "std                            3.30                   11.62  \n",
       "min                            1.00                    1.00  \n",
       "25%                            3.00                    8.00  \n",
       "50%                            5.00                   20.00  \n",
       "75%                            9.00                   30.00  \n",
       "max                           11.00                   41.00  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "restaurants.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is just giving us the numerical columns. What else is in the data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['business_id', 'business_name', 'business_address', 'business_city',\n",
       "       'business_state', 'business_postal_code', 'business_latitude',\n",
       "       'business_longitude', 'business_location', 'business_phone_number',\n",
       "       'inspection_id', 'inspection_date', 'inspection_score',\n",
       "       'inspection_type', 'violation_id', 'violation_description',\n",
       "       'risk_category', 'Neighborhoods', 'SF Find Neighborhoods',\n",
       "       'Current Police Districts', 'Current Supervisor Districts',\n",
       "       'Analysis Neighborhoods'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "restaurants.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>risk_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Low Risk</th>\n",
       "      <td>19112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moderate Risk</th>\n",
       "      <td>15316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High Risk</th>\n",
       "      <td>5825</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               risk_category\n",
       "Low Risk               19112\n",
       "Moderate Risk          15316\n",
       "High Risk               5825"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "restaurants['risk_category'].value_counts().to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>violation_description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Unclean or degraded floors walls or ceilings</th>\n",
       "      <td>3453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unapproved or unmaintained equipment or utensils</th>\n",
       "      <td>2917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inadequately cleaned or sanitized food contact surfaces</th>\n",
       "      <td>2823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inadequate and inaccessible handwashing facilities</th>\n",
       "      <td>2727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moderate risk food holding temperature</th>\n",
       "      <td>2623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Improper cooking time or temperatures</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mobile food facility stored in unapproved location</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Improperly displayed mobile food permit or signage</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Noncompliance with Gulf Coast oyster regulation</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No restroom facility within 200 feet of mobile food facility</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>65 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    violation_description\n",
       "Unclean or degraded floors walls or ceilings                         3453\n",
       "Unapproved or unmaintained equipment or utensils                     2917\n",
       "Inadequately cleaned or sanitized food contact ...                   2823\n",
       "Inadequate and inaccessible handwashing facilities                   2727\n",
       "Moderate risk food holding temperature                               2623\n",
       "...                                                                   ...\n",
       "Improper cooking time or temperatures                                   6\n",
       "Mobile food facility stored in unapproved location                      5\n",
       "Improperly displayed mobile food permit or signage                      5\n",
       "Noncompliance with Gulf Coast oyster regulation                         4\n",
       "No restroom facility within 200 feet of mobile ...                      1\n",
       "\n",
       "[65 rows x 1 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "restaurants['violation_description'].value_counts().to_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What kinds of violations count as high vs low risk?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>violation_description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>High risk food holding temperature</th>\n",
       "      <td>1620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unclean or unsanitary food contact surfaces</th>\n",
       "      <td>1245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Improper cooling methods</th>\n",
       "      <td>910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High risk vermin infestation</th>\n",
       "      <td>822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unclean hands or improper use of gloves</th>\n",
       "      <td>660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No hot water or running water</th>\n",
       "      <td>159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Contaminated or adulterated food</th>\n",
       "      <td>133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Improper reheating of food</th>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sewage or wastewater contamination</th>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unauthorized or unsafe use of time as a public health control measure</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other high risk violation</th>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mobile food facility not operating with an approved commissary</th>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unapproved food source</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mobile food facility with unapproved operating conditions</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Improper cooking time or temperatures</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No restroom facility within 200 feet of mobile food facility</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    violation_description\n",
       "High risk food holding temperature                                   1620\n",
       "Unclean or unsanitary food contact surfaces                          1245\n",
       "Improper cooling methods                                              910\n",
       "High risk vermin infestation                                          822\n",
       "Unclean hands or improper use of gloves                               660\n",
       "No hot water or running water                                         159\n",
       "Contaminated or adulterated food                                      133\n",
       "Improper reheating of food                                            120\n",
       "Sewage or wastewater contamination                                     36\n",
       "Unauthorized or unsafe use of time as a public ...                     34\n",
       "Other high risk violation                                              28\n",
       "Mobile food facility not operating with an appr...                     24\n",
       "Unapproved food source                                                 17\n",
       "Mobile food facility with unapproved operating ...                     10\n",
       "Improper cooking time or temperatures                                   6\n",
       "No restroom facility within 200 feet of mobile ...                      1"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "restaurants.query('risk_category == \"High Risk\"')['violation_description'].value_counts().to_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What happens if there's no violation?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>risk_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Low Risk</th>\n",
       "      <td>19112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moderate Risk</th>\n",
       "      <td>15316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>13720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High Risk</th>\n",
       "      <td>5825</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               risk_category\n",
       "Low Risk               19112\n",
       "Moderate Risk          15316\n",
       "NaN                    13720\n",
       "High Risk               5825"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "restaurants['risk_category'].value_counts(dropna=False).to_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What do we think the NaN values reflect?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>risk_category</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>1987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Low Risk</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moderate Risk</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High Risk</th>\n",
       "      <td>2</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               risk_category\n",
       "NaN                     1987\n",
       "Low Risk                   9\n",
       "Moderate Risk              4\n",
       "High Risk                  2"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "restaurants.query('inspection_score == 100')['risk_category'].value_counts(dropna=False).to_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Can one inspection have multiple violations?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>984_20170726</th>\n",
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       "      <th>10877_20190701</th>\n",
       "      <td>14</td>\n",
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       "      <th>91843_20180822</th>\n",
       "      <td>14</td>\n",
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       "      <th>76218_20190124</th>\n",
       "      <td>14</td>\n",
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       "      <th>...</th>\n",
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       "      <th>71131_20161012</th>\n",
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       "      <th>5808_20190918</th>\n",
       "      <td>1</td>\n",
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       "      <th>27821_20180720</th>\n",
       "      <td>1</td>\n",
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       "<p>26663 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 inspection_id\n",
       "97283_20180823              15\n",
       "984_20170726                14\n",
       "10877_20190701              14\n",
       "91843_20180822              14\n",
       "76218_20190124              14\n",
       "...                        ...\n",
       "100274_20190509              1\n",
       "71131_20161012               1\n",
       "5938_20170921                1\n",
       "5808_20190918                1\n",
       "27821_20180720               1\n",
       "\n",
       "[26663 rows x 1 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "restaurants['inspection_id'].value_counts().to_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What happened with this restaurant that had 15 violations?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>97283_20180823_103110</td>\n",
       "      <td>Unapproved food source</td>\n",
       "      <td>High Risk</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>CA</td>\n",
       "      <td>94134</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>08/23/2018 12:00:00 AM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>New Ownership</td>\n",
       "      <td>97283_20180823_103130</td>\n",
       "      <td>Inadequate sewage or wastewater disposal</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>97283</td>\n",
       "      <td>PICCOLO PETE</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>08/23/2018 12:00:00 AM</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>97283_20180823_103151</td>\n",
       "      <td>Inadequate or unsanitary refuse containers or ...</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>94134</td>\n",
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       "      <td>08/23/2018 12:00:00 AM</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>97283_20180823_103154</td>\n",
       "      <td>Unclean or degraded floors walls or ceilings</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>43773</th>\n",
       "      <td>97283</td>\n",
       "      <td>PICCOLO PETE</td>\n",
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       "      <td>CA</td>\n",
       "      <td>94134</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>97283_20180823</td>\n",
       "      <td>08/23/2018 12:00:00 AM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>New Ownership</td>\n",
       "      <td>97283_20180823_103116</td>\n",
       "      <td>Inadequate food safety knowledge or lack of ce...</td>\n",
       "      <td>Moderate Risk</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44117</th>\n",
       "      <td>97283</td>\n",
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       "      <td>2155 Bayshore Blvd</td>\n",
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       "      <td>CA</td>\n",
       "      <td>94134</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>08/23/2018 12:00:00 AM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>New Ownership</td>\n",
       "      <td>97283_20180823_103119</td>\n",
       "      <td>Inadequate and inaccessible handwashing facili...</td>\n",
       "      <td>Moderate Risk</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45128</th>\n",
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       "      <td>94134</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>08/23/2018 12:00:00 AM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>New Ownership</td>\n",
       "      <td>97283_20180823_103143</td>\n",
       "      <td>Inadequate warewashing facilities or equipment</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46756</th>\n",
       "      <td>97283</td>\n",
       "      <td>PICCOLO PETE</td>\n",
       "      <td>2155 Bayshore Blvd</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94134</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14155793720.00</td>\n",
       "      <td>97283_20180823</td>\n",
       "      <td>08/23/2018 12:00:00 AM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>New Ownership</td>\n",
       "      <td>97283_20180823_103142</td>\n",
       "      <td>Unclean nonfood contact surfaces</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47632</th>\n",
       "      <td>97283</td>\n",
       "      <td>PICCOLO PETE</td>\n",
       "      <td>2155 Bayshore Blvd</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94134</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14155793720.00</td>\n",
       "      <td>97283_20180823</td>\n",
       "      <td>08/23/2018 12:00:00 AM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>New Ownership</td>\n",
       "      <td>97283_20180823_103165</td>\n",
       "      <td>No plan review or Building Permit</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47855</th>\n",
       "      <td>97283</td>\n",
       "      <td>PICCOLO PETE</td>\n",
       "      <td>2155 Bayshore Blvd</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94134</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14155793720.00</td>\n",
       "      <td>97283_20180823</td>\n",
       "      <td>08/23/2018 12:00:00 AM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>New Ownership</td>\n",
       "      <td>97283_20180823_103147</td>\n",
       "      <td>Inadequate ventilation or lighting</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49987</th>\n",
       "      <td>97283</td>\n",
       "      <td>PICCOLO PETE</td>\n",
       "      <td>2155 Bayshore Blvd</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94134</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14155793720.00</td>\n",
       "      <td>97283_20180823</td>\n",
       "      <td>08/23/2018 12:00:00 AM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>New Ownership</td>\n",
       "      <td>97283_20180823_103149</td>\n",
       "      <td>Wiping cloths not clean or properly stored or ...</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51065</th>\n",
       "      <td>97283</td>\n",
       "      <td>PICCOLO PETE</td>\n",
       "      <td>2155 Bayshore Blvd</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94134</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14155793720.00</td>\n",
       "      <td>97283_20180823</td>\n",
       "      <td>08/23/2018 12:00:00 AM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>New Ownership</td>\n",
       "      <td>97283_20180823_103103</td>\n",
       "      <td>High risk food holding temperature</td>\n",
       "      <td>High Risk</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52654</th>\n",
       "      <td>97283</td>\n",
       "      <td>PICCOLO PETE</td>\n",
       "      <td>2155 Bayshore Blvd</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94134</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14155793720.00</td>\n",
       "      <td>97283_20180823</td>\n",
       "      <td>08/23/2018 12:00:00 AM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>New Ownership</td>\n",
       "      <td>97283_20180823_103144</td>\n",
       "      <td>Unapproved or unmaintained equipment or utensils</td>\n",
       "      <td>Low Risk</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53482</th>\n",
       "      <td>97283</td>\n",
       "      <td>PICCOLO PETE</td>\n",
       "      <td>2155 Bayshore Blvd</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94134</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14155793720.00</td>\n",
       "      <td>97283_20180823</td>\n",
       "      <td>08/23/2018 12:00:00 AM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>New Ownership</td>\n",
       "      <td>97283_20180823_103133</td>\n",
       "      <td>Foods not protected from contamination</td>\n",
       "      <td>Moderate Risk</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       business_id business_name    business_address  business_city  \\\n",
       "38245        97283  PICCOLO PETE  2155 Bayshore Blvd  San Francisco   \n",
       "39468        97283  PICCOLO PETE  2155 Bayshore Blvd  San Francisco   \n",
       "40357        97283  PICCOLO PETE  2155 Bayshore Blvd  San Francisco   \n",
       "41306        97283  PICCOLO PETE  2155 Bayshore Blvd  San Francisco   \n",
       "42369        97283  PICCOLO PETE  2155 Bayshore Blvd  San Francisco   \n",
       "43773        97283  PICCOLO PETE  2155 Bayshore Blvd  San Francisco   \n",
       "44117        97283  PICCOLO PETE  2155 Bayshore Blvd  San Francisco   \n",
       "45128        97283  PICCOLO PETE  2155 Bayshore Blvd  San Francisco   \n",
       "46756        97283  PICCOLO PETE  2155 Bayshore Blvd  San Francisco   \n",
       "47632        97283  PICCOLO PETE  2155 Bayshore Blvd  San Francisco   \n",
       "47855        97283  PICCOLO PETE  2155 Bayshore Blvd  San Francisco   \n",
       "49987        97283  PICCOLO PETE  2155 Bayshore Blvd  San Francisco   \n",
       "51065        97283  PICCOLO PETE  2155 Bayshore Blvd  San Francisco   \n",
       "52654        97283  PICCOLO PETE  2155 Bayshore Blvd  San Francisco   \n",
       "53482        97283  PICCOLO PETE  2155 Bayshore Blvd  San Francisco   \n",
       "\n",
       "      business_state business_postal_code  business_latitude  \\\n",
       "38245             CA                94134                NaN   \n",
       "39468             CA                94134                NaN   \n",
       "40357             CA                94134                NaN   \n",
       "41306             CA                94134                NaN   \n",
       "42369             CA                94134                NaN   \n",
       "43773             CA                94134                NaN   \n",
       "44117             CA                94134                NaN   \n",
       "45128             CA                94134                NaN   \n",
       "46756             CA                94134                NaN   \n",
       "47632             CA                94134                NaN   \n",
       "47855             CA                94134                NaN   \n",
       "49987             CA                94134                NaN   \n",
       "51065             CA                94134                NaN   \n",
       "52654             CA                94134                NaN   \n",
       "53482             CA                94134                NaN   \n",
       "\n",
       "       business_longitude business_location  business_phone_number  \\\n",
       "38245                 NaN               NaN         14155793720.00   \n",
       "39468                 NaN               NaN         14155793720.00   \n",
       "40357                 NaN               NaN         14155793720.00   \n",
       "41306                 NaN               NaN         14155793720.00   \n",
       "42369                 NaN               NaN         14155793720.00   \n",
       "43773                 NaN               NaN         14155793720.00   \n",
       "44117                 NaN               NaN         14155793720.00   \n",
       "45128                 NaN               NaN         14155793720.00   \n",
       "46756                 NaN               NaN         14155793720.00   \n",
       "47632                 NaN               NaN         14155793720.00   \n",
       "47855                 NaN               NaN         14155793720.00   \n",
       "49987                 NaN               NaN         14155793720.00   \n",
       "51065                 NaN               NaN         14155793720.00   \n",
       "52654                 NaN               NaN         14155793720.00   \n",
       "53482                 NaN               NaN         14155793720.00   \n",
       "\n",
       "        inspection_id         inspection_date  inspection_score  \\\n",
       "38245  97283_20180823  08/23/2018 12:00:00 AM               NaN   \n",
       "39468  97283_20180823  08/23/2018 12:00:00 AM               NaN   \n",
       "40357  97283_20180823  08/23/2018 12:00:00 AM               NaN   \n",
       "41306  97283_20180823  08/23/2018 12:00:00 AM               NaN   \n",
       "42369  97283_20180823  08/23/2018 12:00:00 AM               NaN   \n",
       "43773  97283_20180823  08/23/2018 12:00:00 AM               NaN   \n",
       "44117  97283_20180823  08/23/2018 12:00:00 AM               NaN   \n",
       "45128  97283_20180823  08/23/2018 12:00:00 AM               NaN   \n",
       "46756  97283_20180823  08/23/2018 12:00:00 AM               NaN   \n",
       "47632  97283_20180823  08/23/2018 12:00:00 AM               NaN   \n",
       "47855  97283_20180823  08/23/2018 12:00:00 AM               NaN   \n",
       "49987  97283_20180823  08/23/2018 12:00:00 AM               NaN   \n",
       "51065  97283_20180823  08/23/2018 12:00:00 AM               NaN   \n",
       "52654  97283_20180823  08/23/2018 12:00:00 AM               NaN   \n",
       "53482  97283_20180823  08/23/2018 12:00:00 AM               NaN   \n",
       "\n",
       "      inspection_type           violation_id  \\\n",
       "38245   New Ownership  97283_20180823_103150   \n",
       "39468   New Ownership  97283_20180823_103110   \n",
       "40357   New Ownership  97283_20180823_103130   \n",
       "41306   New Ownership  97283_20180823_103151   \n",
       "42369   New Ownership  97283_20180823_103154   \n",
       "43773   New Ownership  97283_20180823_103116   \n",
       "44117   New Ownership  97283_20180823_103119   \n",
       "45128   New Ownership  97283_20180823_103143   \n",
       "46756   New Ownership  97283_20180823_103142   \n",
       "47632   New Ownership  97283_20180823_103165   \n",
       "47855   New Ownership  97283_20180823_103147   \n",
       "49987   New Ownership  97283_20180823_103149   \n",
       "51065   New Ownership  97283_20180823_103103   \n",
       "52654   New Ownership  97283_20180823_103144   \n",
       "53482   New Ownership  97283_20180823_103133   \n",
       "\n",
       "                                   violation_description  risk_category  \\\n",
       "38245                     Improper or defective plumbing       Low Risk   \n",
       "39468                             Unapproved food source      High Risk   \n",
       "40357           Inadequate sewage or wastewater disposal  Moderate Risk   \n",
       "41306  Inadequate or unsanitary refuse containers or ...       Low Risk   \n",
       "42369       Unclean or degraded floors walls or ceilings       Low Risk   \n",
       "43773  Inadequate food safety knowledge or lack of ce...  Moderate Risk   \n",
       "44117  Inadequate and inaccessible handwashing facili...  Moderate Risk   \n",
       "45128     Inadequate warewashing facilities or equipment       Low Risk   \n",
       "46756                   Unclean nonfood contact surfaces       Low Risk   \n",
       "47632                  No plan review or Building Permit       Low Risk   \n",
       "47855                 Inadequate ventilation or lighting       Low Risk   \n",
       "49987  Wiping cloths not clean or properly stored or ...       Low Risk   \n",
       "51065                 High risk food holding temperature      High Risk   \n",
       "52654   Unapproved or unmaintained equipment or utensils       Low Risk   \n",
       "53482             Foods not protected from contamination  Moderate Risk   \n",
       "\n",
       "       Neighborhoods  SF Find Neighborhoods  Current Police Districts  \\\n",
       "38245            NaN                    NaN                       NaN   \n",
       "39468            NaN                    NaN                       NaN   \n",
       "40357            NaN                    NaN                       NaN   \n",
       "41306            NaN                    NaN                       NaN   \n",
       "42369            NaN                    NaN                       NaN   \n",
       "43773            NaN                    NaN                       NaN   \n",
       "44117            NaN                    NaN                       NaN   \n",
       "45128            NaN                    NaN                       NaN   \n",
       "46756            NaN                    NaN                       NaN   \n",
       "47632            NaN                    NaN                       NaN   \n",
       "47855            NaN                    NaN                       NaN   \n",
       "49987            NaN                    NaN                       NaN   \n",
       "51065            NaN                    NaN                       NaN   \n",
       "52654            NaN                    NaN                       NaN   \n",
       "53482            NaN                    NaN                       NaN   \n",
       "\n",
       "       Current Supervisor Districts  Analysis Neighborhoods  \n",
       "38245                           NaN                     NaN  \n",
       "39468                           NaN                     NaN  \n",
       "40357                           NaN                     NaN  \n",
       "41306                           NaN                     NaN  \n",
       "42369                           NaN                     NaN  \n",
       "43773                           NaN                     NaN  \n",
       "44117                           NaN                     NaN  \n",
       "45128                           NaN                     NaN  \n",
       "46756                           NaN                     NaN  \n",
       "47632                           NaN                     NaN  \n",
       "47855                           NaN                     NaN  \n",
       "49987                           NaN                     NaN  \n",
       "51065                           NaN                     NaN  \n",
       "52654                           NaN                     NaN  \n",
       "53482                           NaN                     NaN  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "restaurants.query('inspection_id == \"97283_20180823\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>risk_category</th>\n",
       "      <th>High Risk</th>\n",
       "      <th>Low Risk</th>\n",
       "      <th>Moderate Risk</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inspection_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>97283_20180823</th>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97283_20190604</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "risk_category   High Risk  Low Risk  Moderate Risk\n",
       "inspection_id                                     \n",
       "97283_20180823          2         9              4\n",
       "97283_20190604          0         0              4"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(restaurants.query('business_name == \"PICCOLO PETE\"'),\n",
    "            index = 'inspection_id',\n",
    "            columns = 'risk_category',\n",
    "            values = 'business_id',\n",
    "            aggfunc = 'count',\n",
    "            dropna =False,\n",
    "            fill_value = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Did we miss anything by making this pivot table?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>inspection_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>97283_20180823</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97283_20190604</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97283_20190716</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97283_20190626</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97283_20180913</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                inspection_id\n",
       "97283_20180823             15\n",
       "97283_20190604              4\n",
       "97283_20190716              1\n",
       "97283_20190626              1\n",
       "97283_20180913              1"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "restaurants.query('business_name == \"PICCOLO PETE\"')['inspection_id'].value_counts().to_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To be safer, we could *either*\n",
    "1. Learn the rules around NaN really really carefully\n",
    "2. Make a new risk category column where NaN is represented as 'No Risk Category' or something"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "\n",
    "\n",
    "\n",
    "\n",
    "## 2) JSON\n",
    "The City of Berkeley Open Data [website](https://data.cityofberkeley.info/Health/COVID-19-Confirmed-Cases/xn6j-b766) has a dataset with COVID-19 Confirmed Cases among Berkeley residents by date.\n",
    "\n",
    "Let's download this file, saving it as a JSON (note the source URL file type):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using cached version that was downloaded (UTC): Tue Sep  6 02:37:41 2022\n"
     ]
    }
   ],
   "source": [
    "covid_file = fetch_and_cache(\n",
    "    \"https://data.cityofberkeley.info/api/views/xn6j-b766/rows.json?accessType=DOWNLOAD\",\n",
    "    \"confirmed-cases.json\",\n",
    "    force=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### 1. How big is the data file?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data/confirmed-cases.json is 0.116367 MB\n",
      "data/confirmed-cases.json is 1110 lines.\n"
     ]
    }
   ],
   "source": [
    "print(covid_file, \"is\", os.path.getsize(covid_file) / 1e6, \"MB\")\n",
    "\n",
    "with open(covid_file, \"r\") as f:\n",
    "    print(covid_file, \"is\", sum(1 for l in f), \"lines.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br/><br/>\n",
    "### 2. How is the data file formatted?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data/confirmed-cases.json ======================\n",
      "000 | '{\\n'\n",
      "001 | '  \"meta\" : {\\n'\n",
      "002 | '    \"view\" : {\\n'\n",
      "003 | '      \"id\" : \"xn6j-b766\",\\n'\n",
      "004 | '      \"name\" : \"COVID-19 Confirmed Cases\",\\n'\n",
      "005 | '      \"assetType\" : \"dataset\",\\n'\n",
      "006 | '      \"attribution\" : \"City of Berkeley\",\\n'\n",
      "007 | '      \"averageRating\" : 0,\\n'\n",
      "008 | '      \"category\" : \"Health\",\\n'\n",
      "009 | '      \"createdAt\" : 1587074071,\\n'\n",
      "010 | '      \"description\" : \"Counts of confirmed COVID-19 cases among Berkeley residents by date.\",\\n'\n",
      "011 | '      \"displayType\" : \"table\",\\n'\n",
      "012 | '      \"downloadCount\" : 2804,\\n'\n",
      "013 | '      \"hideFromCatalog\" : false,\\n'\n",
      "014 | '      \"hideFromDataJson\" : false,\\n'\n",
      "015 | '      \"newBackend\" : true,\\n'\n",
      "016 | '      \"numberOfComments\" : 0,\\n'\n",
      "017 | '      \"oid\" : 37306599,\\n'\n",
      "018 | '      \"provenance\" : \"official\",\\n'\n",
      "019 | '      \"publicationAppendEnabled\" : false,\\n'\n"
     ]
    }
   ],
   "source": [
    "# raw text\n",
    "print(covid_file, \"======================\")\n",
    "with open(covid_file, \"r\") as f:\n",
    "    for i in range(20):\n",
    "        print(f\"{i:03} | {repr(f.readline())}\") # zero pad line numbers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "This appears to be a fairly standard JSON file.  We notice that the file appears to contain a description of itself in a field called \"meta\" (which is presumably short for **metadata**).\n",
    "\n",
    "<br/><br/>\n",
    "### 3. How do we read the file into Pandas?\n",
    "\n",
    "We'll come back to this question. Let's first understand more about the particular structure of this JSON file so that we can decide what (if anything) to load into Pandas. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "---\n",
    "<br/><br/>\n",
    "\n",
    "# Digging into JSON\n",
    "\n",
    "Python has relatively good support for JSON data since it closely matches the internal python object model.  In the following cell we import the entire JSON datafile into a python dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-02T15:15:19.441300Z",
     "start_time": "2018-02-02T15:15:19.358900Z"
    }
   },
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "with open(covid_file, \"rb\") as f:\n",
    "    covid_json = json.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `covid_json` variable is now a dictionary encoding the data in the file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-02T15:15:19.447238Z",
     "start_time": "2018-02-02T15:15:19.443595Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(covid_json)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Examine what keys are in the top level json object\n",
    "\n",
    "We can list the keys to determine what data is stored in the object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-02T15:15:19.680999Z",
     "start_time": "2018-02-02T15:15:19.675696Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['meta', 'data'])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_json.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observation**: The JSON dictionary contains a `meta` key which likely refers to meta data (data about the data).  Meta data often maintained with the data and can be a good source of additional information."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br/>\n",
    "\n",
    "We can investigate the meta data further by examining the keys associated with the metadata."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-02T15:15:19.687295Z",
     "start_time": "2018-02-02T15:15:19.682902Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['view'])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_json['meta'].keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `meta` key contains another dictionary called `view`.  This likely refers to meta-data about a particular \"view\" of some underlying database.  We will learn more about views when we study SQL later in the class.    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-02T15:15:19.693946Z",
     "start_time": "2018-02-02T15:15:19.690139Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['id', 'name', 'assetType', 'attribution', 'averageRating', 'category', 'createdAt', 'description', 'displayType', 'downloadCount', 'hideFromCatalog', 'hideFromDataJson', 'newBackend', 'numberOfComments', 'oid', 'provenance', 'publicationAppendEnabled', 'publicationDate', 'publicationGroup', 'publicationStage', 'rowsUpdatedAt', 'rowsUpdatedBy', 'tableId', 'totalTimesRated', 'viewCount', 'viewLastModified', 'viewType', 'approvals', 'columns', 'grants', 'metadata', 'owner', 'query', 'rights', 'tableAuthor', 'tags', 'flags'])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_json['meta']['view'].keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that this a nested/recursive data structure.  As we dig deeper we reveal more and more keys and the corresponding data:\n",
    "\n",
    "```\n",
    "meta\n",
    "|-> data\n",
    "    | ... (haven't explored yet)\n",
    "|-> view\n",
    "    | -> id\n",
    "    | -> name\n",
    "    | -> attribution \n",
    "    ...\n",
    "    | -> description\n",
    "    ...\n",
    "    | -> columns\n",
    "    ...\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a key called description in the view sub dictionary.  This likely contains a description of the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-02T15:15:19.699773Z",
     "start_time": "2018-02-02T15:15:19.695818Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counts of confirmed COVID-19 cases among Berkeley residents by date.\n"
     ]
    }
   ],
   "source": [
    "print(covid_json['meta']['view']['description'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br/>\n",
    "\n",
    "### Columns Metadata\n",
    "\n",
    "Another potentially useful key in the metadata dictionary is the `columns`.  This returns a list:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-02T15:15:19.706171Z",
     "start_time": "2018-02-02T15:15:19.701580Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(covid_json['meta']['view']['columns'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can browse summary data in the list using python:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations**\n",
    "\n",
    "1. The above meta data tells us a lot about the columns in the data including column names, potential data anomalies, and a basic statistic. \n",
    "1. JSON makes it easier (than CSV) to create **self-documented data.** \n",
    "1. Self documenting data can be helpful since it maintains its own description and these descriptions are more likely to be updated as data changes. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Examining the Data Field for Records\n",
    "\n",
    "We can look at a few entries in the `data` field. This is what we'll load into Pandas.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-02T15:15:19.722451Z",
     "start_time": "2018-02-02T15:15:19.717198Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "000 | ['row-kzbg.v7my-c3y2', '00000000-0000-0000-0405-CB14DE51DAA7', 0, 1643733903, None, 1643733903, None, '{ }', '2020-02-28T00:00:00', '1', '1']\n",
      "001 | ['row-jkyx_9u4r-h2yw', '00000000-0000-0000-F806-86D0DBE0E17F', 0, 1643733903, None, 1643733903, None, '{ }', '2020-02-29T00:00:00', '0', '1']\n",
      "002 | ['row-qifg_4aug-y3ym', '00000000-0000-0000-2DCE-4D1872F9B216', 0, 1643733903, None, 1643733903, None, '{ }', '2020-03-01T00:00:00', '0', '1']\n"
     ]
    }
   ],
   "source": [
    "for i in range(3):\n",
    "    print(f\"{i:03} | {covid_json['data'][i]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<br/>\n",
    "\n",
    "### 3. How do we read the file into Pandas? Building a DataFrame from JSON\n",
    "\n",
    "Let's understand more about the particular structure of this JSON file so that we can decide what (if anything) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the following block of code we:\n",
    "1. Translate the JSON records into a dataframe:\n",
    "\n",
    "    * fields: `covid_json['meta']['view']['columns']`\n",
    "    * records: `covid_json['data']`\n",
    "    \n",
    "1. Remove columns that have no metadata description.  This would be a bad idea in general but here we remove these columns since the above analysis suggests that they are unlikely to contain useful information.\n",
    "1. Examine the table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-02T15:15:19.793306Z",
     "start_time": "2018-02-02T15:15:19.725044Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sid</th>\n",
       "      <th>id</th>\n",
       "      <th>position</th>\n",
       "      <th>created_at</th>\n",
       "      <th>created_meta</th>\n",
       "      <th>updated_at</th>\n",
       "      <th>updated_meta</th>\n",
       "      <th>meta</th>\n",
       "      <th>Date</th>\n",
       "      <th>New Cases</th>\n",
       "      <th>Cumulative Cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>699</th>\n",
       "      <td>row-49b6_x8zv.gyum</td>\n",
       "      <td>00000000-0000-0000-A18C-9174A6D05774</td>\n",
       "      <td>0</td>\n",
       "      <td>1643733903</td>\n",
       "      <td>None</td>\n",
       "      <td>1643733903</td>\n",
       "      <td>None</td>\n",
       "      <td>{ }</td>\n",
       "      <td>2022-01-27T00:00:00</td>\n",
       "      <td>106</td>\n",
       "      <td>10694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>700</th>\n",
       "      <td>row-gs55-p5em.y4v9</td>\n",
       "      <td>00000000-0000-0000-F41D-5724AEABB4D6</td>\n",
       "      <td>0</td>\n",
       "      <td>1643733903</td>\n",
       "      <td>None</td>\n",
       "      <td>1643733903</td>\n",
       "      <td>None</td>\n",
       "      <td>{ }</td>\n",
       "      <td>2022-01-28T00:00:00</td>\n",
       "      <td>223</td>\n",
       "      <td>10917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>701</th>\n",
       "      <td>row-3pyj.tf95-qu67</td>\n",
       "      <td>00000000-0000-0000-BEE3-B0188D2518BD</td>\n",
       "      <td>0</td>\n",
       "      <td>1643733903</td>\n",
       "      <td>None</td>\n",
       "      <td>1643733903</td>\n",
       "      <td>None</td>\n",
       "      <td>{ }</td>\n",
       "      <td>2022-01-29T00:00:00</td>\n",
       "      <td>139</td>\n",
       "      <td>11056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>702</th>\n",
       "      <td>row-cgnd.8syv.jvjn</td>\n",
       "      <td>00000000-0000-0000-C318-63CF75F7F740</td>\n",
       "      <td>0</td>\n",
       "      <td>1643733903</td>\n",
       "      <td>None</td>\n",
       "      <td>1643733903</td>\n",
       "      <td>None</td>\n",
       "      <td>{ }</td>\n",
       "      <td>2022-01-30T00:00:00</td>\n",
       "      <td>33</td>\n",
       "      <td>11089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>703</th>\n",
       "      <td>row-qywv_24x6-237y</td>\n",
       "      <td>00000000-0000-0000-FE92-9789FED3AA20</td>\n",
       "      <td>0</td>\n",
       "      <td>1643733903</td>\n",
       "      <td>None</td>\n",
       "      <td>1643733903</td>\n",
       "      <td>None</td>\n",
       "      <td>{ }</td>\n",
       "      <td>2022-01-31T00:00:00</td>\n",
       "      <td>42</td>\n",
       "      <td>11131</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    sid                                    id  position  \\\n",
       "699  row-49b6_x8zv.gyum  00000000-0000-0000-A18C-9174A6D05774         0   \n",
       "700  row-gs55-p5em.y4v9  00000000-0000-0000-F41D-5724AEABB4D6         0   \n",
       "701  row-3pyj.tf95-qu67  00000000-0000-0000-BEE3-B0188D2518BD         0   \n",
       "702  row-cgnd.8syv.jvjn  00000000-0000-0000-C318-63CF75F7F740         0   \n",
       "703  row-qywv_24x6-237y  00000000-0000-0000-FE92-9789FED3AA20         0   \n",
       "\n",
       "     created_at created_meta  updated_at updated_meta meta  \\\n",
       "699  1643733903         None  1643733903         None  { }   \n",
       "700  1643733903         None  1643733903         None  { }   \n",
       "701  1643733903         None  1643733903         None  { }   \n",
       "702  1643733903         None  1643733903         None  { }   \n",
       "703  1643733903         None  1643733903         None  { }   \n",
       "\n",
       "                    Date New Cases Cumulative Cases  \n",
       "699  2022-01-27T00:00:00       106            10694  \n",
       "700  2022-01-28T00:00:00       223            10917  \n",
       "701  2022-01-29T00:00:00       139            11056  \n",
       "702  2022-01-30T00:00:00        33            11089  \n",
       "703  2022-01-31T00:00:00        42            11131  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the data from JSON and assign column titles\n",
    "covid = pd.DataFrame(\n",
    "    covid_json['data'],\n",
    "    columns=[c['name'] for c in covid_json['meta']['view']['columns']])\n",
    "\n",
    "covid.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "---\n",
    "<br/><br/>\n",
    "\n",
    "## Your turn: Mauna Loa CO2 data\n",
    "\n",
    "CO2 concentrations have been monitored at Mauna Loa Observatory since 1958 ([website link](https://gml.noaa.gov/ccgg/trends/data.html)).\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "co2_file = \"data/co2_mm_mlo.txt\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data/co2_mm_mlo.txt is 0.051131 MB\n",
      "data/co2_mm_mlo.txt is 810 lines.\n"
     ]
    }
   ],
   "source": [
    "print(co2_file, \"is\", os.path.getsize(co2_file) / 1e6, \"MB\")\n",
    "\n",
    "with open(co2_file, \"r\") as f:\n",
    "    print(co2_file, \"is\", sum(1 for l in f), \"lines.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### How do we read the file into Pandas?\n",
    "While we could use Python again to check out the contents, let's instead check out this file with JupyterLab (note it's a `.txt` file. Do we trust this file extension?)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br/><br/>\n",
    "---\n",
    "\n",
    "---\n",
    "<br/><br/><br/><br/><br/>\n",
    "\n",
    "Looking at the first few lines of the data, we spot some relevant characteristics:\n",
    "\n",
    "- The values are separated by white space, possibly tabs.\n",
    "- The data line up down the rows. For example, the month appears in 7th to 8th position of each line.\n",
    "- The 71st and 72nd lines in the file contain column headings split over two lines.\n",
    "\n",
    "We can use `read_csv` to read the data into a Pandas data frame, and we provide several arguments to specify that the separators are white space, there is no header (**we will set our own column names**), and to skip the first 72 rows of the file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>1958</td>\n",
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       "      <th>1</th>\n",
       "      <td>1958</td>\n",
       "      <td>4</td>\n",
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       "      <td>317.45</td>\n",
       "      <td>317.45</td>\n",
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       "      <th>2</th>\n",
       "      <td>1958</td>\n",
       "      <td>5</td>\n",
       "      <td>1958.38</td>\n",
       "      <td>317.50</td>\n",
       "      <td>317.50</td>\n",
       "      <td>314.71</td>\n",
       "      <td>-1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1958</td>\n",
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       "      <td>1958.46</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>317.10</td>\n",
       "      <td>314.85</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1958</td>\n",
       "      <td>7</td>\n",
       "      <td>1958.54</td>\n",
       "      <td>315.86</td>\n",
       "      <td>315.86</td>\n",
       "      <td>314.98</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      0  1       2      3      4      5  6\n",
       "0  1958  3 1958.21 315.71 315.71 314.62 -1\n",
       "1  1958  4 1958.29 317.45 317.45 315.29 -1\n",
       "2  1958  5 1958.38 317.50 317.50 314.71 -1\n",
       "3  1958  6 1958.46 -99.99 317.10 314.85 -1\n",
       "4  1958  7 1958.54 315.86 315.86 314.98 -1"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "co2 = pd.read_csv(\n",
    "    co2_file, header = None, skiprows = 72,\n",
    "    sep = '\\s+' #regex for continuous whitespace (next lecture)\n",
    ")\n",
    "co2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Congratulations! You've wrangled your first set of real world data!\n",
    "\n",
    "<br/>\n",
    "\n",
    "...But our columns aren't named.\n",
    "**We need to do more EDA.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br/><br/>\n",
    "---\n",
    "# Real World Example: Wrangling CO2 Measurements\n",
    "\n",
    "Let's continue looking at the CO2 concentrations at Mauna Loa Observatory:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1958</td>\n",
       "      <td>3</td>\n",
       "      <td>1958.21</td>\n",
       "      <td>315.71</td>\n",
       "      <td>315.71</td>\n",
       "      <td>314.62</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1958</td>\n",
       "      <td>4</td>\n",
       "      <td>1958.29</td>\n",
       "      <td>317.45</td>\n",
       "      <td>317.45</td>\n",
       "      <td>315.29</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1958</td>\n",
       "      <td>5</td>\n",
       "      <td>1958.38</td>\n",
       "      <td>317.50</td>\n",
       "      <td>317.50</td>\n",
       "      <td>314.71</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1958</td>\n",
       "      <td>6</td>\n",
       "      <td>1958.46</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>317.10</td>\n",
       "      <td>314.85</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1958</td>\n",
       "      <td>7</td>\n",
       "      <td>1958.54</td>\n",
       "      <td>315.86</td>\n",
       "      <td>315.86</td>\n",
       "      <td>314.98</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      0  1       2      3      4      5  6\n",
       "0  1958  3 1958.21 315.71 315.71 314.62 -1\n",
       "1  1958  4 1958.29 317.45 317.45 315.29 -1\n",
       "2  1958  5 1958.38 317.50 317.50 314.71 -1\n",
       "3  1958  6 1958.46 -99.99 317.10 314.85 -1\n",
       "4  1958  7 1958.54 315.86 315.86 314.98 -1"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "co2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need column names!\n",
    "\n",
    "---\n",
    "## Exploring Variable Feature Types\n",
    "Let's go back to the raw data file to understand each feature.\n",
    "\n",
    "The NOAA [webpage](https://gml.noaa.gov/ccgg/trends/) might have some useful tidbits (in this case it doesn't).\n",
    "\n",
    "<br/><br/>\n",
    "We'll rerun `pd.read_csv`, but this time with some **custom column names.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Yr</th>\n",
       "      <th>Mo</th>\n",
       "      <th>DecDate</th>\n",
       "      <th>Avg</th>\n",
       "      <th>Int</th>\n",
       "      <th>Trend</th>\n",
       "      <th>Days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1958</td>\n",
       "      <td>3</td>\n",
       "      <td>1958.21</td>\n",
       "      <td>315.71</td>\n",
       "      <td>315.71</td>\n",
       "      <td>314.62</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1958</td>\n",
       "      <td>4</td>\n",
       "      <td>1958.29</td>\n",
       "      <td>317.45</td>\n",
       "      <td>317.45</td>\n",
       "      <td>315.29</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1958</td>\n",
       "      <td>5</td>\n",
       "      <td>1958.38</td>\n",
       "      <td>317.50</td>\n",
       "      <td>317.50</td>\n",
       "      <td>314.71</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1958</td>\n",
       "      <td>6</td>\n",
       "      <td>1958.46</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>317.10</td>\n",
       "      <td>314.85</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1958</td>\n",
       "      <td>7</td>\n",
       "      <td>1958.54</td>\n",
       "      <td>315.86</td>\n",
       "      <td>315.86</td>\n",
       "      <td>314.98</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Yr  Mo  DecDate    Avg    Int  Trend  Days\n",
       "0  1958   3  1958.21 315.71 315.71 314.62    -1\n",
       "1  1958   4  1958.29 317.45 317.45 315.29    -1\n",
       "2  1958   5  1958.38 317.50 317.50 314.71    -1\n",
       "3  1958   6  1958.46 -99.99 317.10 314.85    -1\n",
       "4  1958   7  1958.54 315.86 315.86 314.98    -1"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "co2 = pd.read_csv(\n",
    "    co2_file, header = None, skiprows = 72,\n",
    "    sep = '\\s+', #regex for continuous whitespace (next lecture)\n",
    "    names = ['Yr', 'Mo', 'DecDate', 'Avg', 'Int', 'Trend', 'Days']\n",
    ")\n",
    "co2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br/><br/>\n",
    "\n",
    "---\n",
    "\n",
    "## Let's start exploring!!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scientific studies tend to have very clean data, right? Let's jump right in and make a time series plot of CO2 monthly averages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x='DecDate', y='Avg', data=co2);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code above uses the `seaborn` plotting library (abbreviated `sns`).\n",
    "We won't cover this library in detail until next week, so focus\n",
    "on the plots themselves, not the code used to create them.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yikes! Plotting the data uncovered a problem. It looks like we have some **missing values**. What happened here? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Yr</th>\n",
       "      <th>Mo</th>\n",
       "      <th>DecDate</th>\n",
       "      <th>Avg</th>\n",
       "      <th>Int</th>\n",
       "      <th>Trend</th>\n",
       "      <th>Days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1958</td>\n",
       "      <td>3</td>\n",
       "      <td>1958.21</td>\n",
       "      <td>315.71</td>\n",
       "      <td>315.71</td>\n",
       "      <td>314.62</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1958</td>\n",
       "      <td>4</td>\n",
       "      <td>1958.29</td>\n",
       "      <td>317.45</td>\n",
       "      <td>317.45</td>\n",
       "      <td>315.29</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1958</td>\n",
       "      <td>5</td>\n",
       "      <td>1958.38</td>\n",
       "      <td>317.50</td>\n",
       "      <td>317.50</td>\n",
       "      <td>314.71</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1958</td>\n",
       "      <td>6</td>\n",
       "      <td>1958.46</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>317.10</td>\n",
       "      <td>314.85</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1958</td>\n",
       "      <td>7</td>\n",
       "      <td>1958.54</td>\n",
       "      <td>315.86</td>\n",
       "      <td>315.86</td>\n",
       "      <td>314.98</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Yr  Mo  DecDate    Avg    Int  Trend  Days\n",
       "0  1958   3  1958.21 315.71 315.71 314.62    -1\n",
       "1  1958   4  1958.29 317.45 317.45 315.29    -1\n",
       "2  1958   5  1958.38 317.50 317.50 314.71    -1\n",
       "3  1958   6  1958.46 -99.99 317.10 314.85    -1\n",
       "4  1958   7  1958.54 315.86 315.86 314.98    -1"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "co2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Yr</th>\n",
       "      <th>Mo</th>\n",
       "      <th>DecDate</th>\n",
       "      <th>Avg</th>\n",
       "      <th>Int</th>\n",
       "      <th>Trend</th>\n",
       "      <th>Days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>733</th>\n",
       "      <td>2019</td>\n",
       "      <td>4</td>\n",
       "      <td>2019.29</td>\n",
       "      <td>413.32</td>\n",
       "      <td>413.32</td>\n",
       "      <td>410.49</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>734</th>\n",
       "      <td>2019</td>\n",
       "      <td>5</td>\n",
       "      <td>2019.38</td>\n",
       "      <td>414.66</td>\n",
       "      <td>414.66</td>\n",
       "      <td>411.20</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>735</th>\n",
       "      <td>2019</td>\n",
       "      <td>6</td>\n",
       "      <td>2019.46</td>\n",
       "      <td>413.92</td>\n",
       "      <td>413.92</td>\n",
       "      <td>411.58</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>736</th>\n",
       "      <td>2019</td>\n",
       "      <td>7</td>\n",
       "      <td>2019.54</td>\n",
       "      <td>411.77</td>\n",
       "      <td>411.77</td>\n",
       "      <td>411.43</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>737</th>\n",
       "      <td>2019</td>\n",
       "      <td>8</td>\n",
       "      <td>2019.62</td>\n",
       "      <td>409.95</td>\n",
       "      <td>409.95</td>\n",
       "      <td>411.84</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Yr  Mo  DecDate    Avg    Int  Trend  Days\n",
       "733  2019   4  2019.29 413.32 413.32 410.49    26\n",
       "734  2019   5  2019.38 414.66 414.66 411.20    28\n",
       "735  2019   6  2019.46 413.92 413.92 411.58    27\n",
       "736  2019   7  2019.54 411.77 411.77 411.43    23\n",
       "737  2019   8  2019.62 409.95 409.95 411.84    29"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "co2.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some data have unusual values like -1 and -99.99.\n",
    "\n",
    "Let's check the description at the top of the file again.\n",
    "1. -1 signifies a missing value for the number of days `Days` the equipment was in operation that month.\n",
    "1. -99.99 denotes a missing monthly average `Avg`\n",
    "\n",
    "How can we fix this? First, let's explore other aspects of our data. Understanding our data will help us decide what to do with the missing values.\n",
    "\n",
    "<br/>\n",
    "\n",
    "---\n",
    "\n",
    "## Quality Checks: Reasoning about the data\n",
    "First, we consider the shape of the data. How many rows should we have?\n",
    "* If chronological order, we should have one record per month.\n",
    "* Data from March 1958 to August 2019.\n",
    "* We should have $ 12 \\times (2019-1957) - 2 - 4 = 738 $ records."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(738, 7)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "co2.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "Nice!! The number of rows (i.e. records) match our expectations.\n",
    "\n",
    "<br/><br/>\n",
    "\n",
    "---\n",
    "\n",
    "Let's now check the quality of each feature.\n",
    "\n",
    "## Understanding Missing Value 1: `Days`\n",
    "`Days` is a time field, so let's analyze other time fields to see if there is an explanation for missing values of days of operation.\n",
    "\n",
    "Let's start with **months** `Mo`.\n",
    "\n",
    "Are we missing any records? The number of months should have 62 or 61 instances (March 1957-August 2019)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     61\n",
       "2     61\n",
       "3     62\n",
       "4     62\n",
       "5     62\n",
       "6     62\n",
       "7     62\n",
       "8     62\n",
       "9     61\n",
       "10    61\n",
       "11    61\n",
       "12    61\n",
       "Name: Mo, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "co2[\"Mo\"].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected Jan, Feb, Sep, Oct, Nov, and Dec have 61 occurrences and the rest 62.\n",
    "\n",
    "<br/><br/>\n",
    "\n",
    "Next let's explore **days** `Days` itself, which is the number of days that the measurement equipment worked."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(co2['Days']);\n",
    "plt.title(\"Distribution of days feature\")\n",
    "plt.show() # suppresses unneeded plotting output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In terms of data quality, a handful of months have averages based on measurements taken on fewer than half the days. In addition, there are nearly 200 missing values--**that's about 27% of the data**!\n",
    "\n",
    "<br/><br/>\n",
    "\n",
    "Finally, let's check the last time feature, **year** `Yr`.\n",
    "\n",
    "Let's check to see if there is any connection between missingness and the year of the recording."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=\"Yr\", y=\"Days\", data=co2);\n",
    "plt.title(\"Day field by Year\"); # the ; suppresses output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations**:\n",
    "\n",
    "* All of the missing data are in the early years of operation.\n",
    "* It appears there may have been problems with equipment in the mid to late 80s.\n",
    "\n",
    "**Potential Next Steps**:\n",
    "* Confirm these explanations through documentation about the historical readings.\n",
    "* Maybe drop earliest recordings? However, we would want to delay such action until after we have examined the time trends and assess whether there are any potential problems.\n",
    "\n",
    "---\n",
    "<br/><br/>\n",
    "\n",
    "## Understanding Missing Value 2: `Avg`\n",
    "Next, let's return to the -99.99 values in `Avg` to analyze the overall quality of the CO2 measurements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histograms of average CO2 measurements\n",
    "sns.displot(co2['Avg']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The non-missing values are in the 300-400 range (a regular range of CO2 levels).\n",
    "\n",
    "We also see that there are only a few missing `Avg` values (**<1% of values**). Let's examine all of them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Yr</th>\n",
       "      <th>Mo</th>\n",
       "      <th>DecDate</th>\n",
       "      <th>Avg</th>\n",
       "      <th>Int</th>\n",
       "      <th>Trend</th>\n",
       "      <th>Days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1958</td>\n",
       "      <td>6</td>\n",
       "      <td>1958.46</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>317.10</td>\n",
       "      <td>314.85</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1958</td>\n",
       "      <td>10</td>\n",
       "      <td>1958.79</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>312.66</td>\n",
       "      <td>315.61</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>1964</td>\n",
       "      <td>2</td>\n",
       "      <td>1964.12</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>320.07</td>\n",
       "      <td>319.61</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>1964</td>\n",
       "      <td>3</td>\n",
       "      <td>1964.21</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>320.73</td>\n",
       "      <td>319.55</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>1964</td>\n",
       "      <td>4</td>\n",
       "      <td>1964.29</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>321.77</td>\n",
       "      <td>319.48</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>1975</td>\n",
       "      <td>12</td>\n",
       "      <td>1975.96</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>330.59</td>\n",
       "      <td>331.60</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>1984</td>\n",
       "      <td>4</td>\n",
       "      <td>1984.29</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>346.84</td>\n",
       "      <td>344.27</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Yr  Mo  DecDate    Avg    Int  Trend  Days\n",
       "3    1958   6  1958.46 -99.99 317.10 314.85    -1\n",
       "7    1958  10  1958.79 -99.99 312.66 315.61    -1\n",
       "71   1964   2  1964.12 -99.99 320.07 319.61    -1\n",
       "72   1964   3  1964.21 -99.99 320.73 319.55    -1\n",
       "73   1964   4  1964.29 -99.99 321.77 319.48    -1\n",
       "213  1975  12  1975.96 -99.99 330.59 331.60     0\n",
       "313  1984   4  1984.29 -99.99 346.84 344.27     2"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "co2[co2[\"Avg\"] < 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There doesn't seem to be a pattern to these values, other than that most records also were missing `Days` data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Drop or Impute Missing `Avg` Data?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remember we want to fix the following plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x='DecDate', y='Avg', data=co2)\n",
    "plt.title(\"CO2 Average By Month\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we are plotting `Avg` vs `DecDate`, we should just focus on dealing with missing values for `Avg`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's consider a few options:\n",
    "1. Drop those records\n",
    "1. Replace -99.99 with NaN\n",
    "1. Substitute it with a likely value for the average CO2?\n",
    "\n",
    "What do you think are the pros and cons of each possible action?\n",
    "\n",
    "---\n",
    "<br/><br/>\n",
    "Let's examine each of these three options."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Drop missing values\n",
    "co2_drop = co2[co2['Avg'] > 0]\n",
    "\n",
    "# 2. Replace NaN with -99.99\n",
    "co2_NA = co2.replace(-99.99, np.NaN)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll also use a third version of the data.\n",
    "First, we note that the dataset already comes with a **substitute value** for the -99.99.\n",
    "\n",
    "From the file description:\n",
    "\n",
    ">  The `interpolated` column includes average values from the preceding column (`average`)\n",
    "and **interpolated values** where data are missing.  Interpolated values are\n",
    "computed in two steps...\n",
    "\n",
    "The `Int` feature has values that exactly match those in `Avg`, except when `Avg` is -99.99, and then a **reasonable** estimate is used instead.\n",
    "So, the third version of our data will use the `Int` feature instead of `Avg`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3. Use interpolated column which estimates missing Avg values\n",
    "co2_impute = co2.copy()\n",
    "co2_impute['Avg'] = co2['Int']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br/>\n",
    "\n",
    "---\n",
    "\n",
    "What's a **reasonable** estimate?\n",
    "\n",
    "To answer this question, let's zoom in on a short time period, say the measurements in 1958 (where we know we have two missing values).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# results of plotting data in 1958\n",
    "\n",
    "def line_and_points(data, ax, title):\n",
    "    # assumes single year, hence Mo\n",
    "    ax.plot('Mo', 'Avg', data=data)\n",
    "    ax.scatter('Mo', 'Avg', data=data)\n",
    "    ax.set_xlim(2, 13)\n",
    "    ax.set_title(title)\n",
    "    ax.set_xticks(np.arange(3, 13))\n",
    "\n",
    "def data_year(data, year):\n",
    "    return data[data[\"Yr\"] == 1958]\n",
    "    \n",
    "# uses matplotlib subplots\n",
    "# you may see more next week; focus on output for now\n",
    "fig, axes = plt.subplots(ncols = 3, figsize=(12, 4), sharey=True)\n",
    "\n",
    "year = 1958\n",
    "line_and_points(data_year(co2_drop, year), axes[0], title=\"1. Drop Missing\")\n",
    "line_and_points(data_year(co2_NA, year), axes[1], title=\"2. Missing Set to NaN\")\n",
    "line_and_points(data_year(co2_impute, year), axes[2], title=\"3. Missing Interpolated\")\n",
    "\n",
    "fig.suptitle(f\"Monthly Averages for {year}\")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the big picture since there are only 7 `Avg` values missing (**<1%** of 738 months), any of these approaches would work.\n",
    "\n",
    "However there is some appeal to **option 3: Imputing**:\n",
    "* Shows seasonal trends for CO2\n",
    "* We are plotting all months in our data as a line plot\n",
    "\n",
    "<br/>\n",
    "\n",
    "---\n",
    "Let's replot our original figure with option 3:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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A4OWXX1Z9aE9l1apVcnp39H/Dhw/H+PHj8a9//Qv5+fl49913UVBQoHjdpZdeCpPJhPr6+oQRhT6fT37AF/95HIpLL70UgHr5wWeffYZwOCyvUVNdXY13330XAPDkk0/Kny0gkrlz3XXXyf+cXnnlFdVvmM8991zcc889cp8PnU6Hu+++GzabDUAkkHSorrrqKkydOhUGgwFA5HN///33ywHCNWvWHPI1D9dVV12Fn//853JgbfDgwXLfEUmS8Je//EURiJk0aRJKSkoAAJs2bVK95tSpUxWNT202G1544QX06dMHDQ0NqqVFHenll1+G3+/H9ddfj7vvvlvRo2Xo0KF46aWXoNPpMH/+fEVflehIy1//+teKAGY0ONe/f/9OvW8iosPFQAERUS+WkZEBAEc86i36QBV9CGkvcSJCW3XSjY2NuOmmm7Bx40ZoNBo8/fTThzQOLlpLPHz48IRvg6MPwS6XK6HEoLi4WC4piM8qmD9/PoLBIE455RSUlpYCiNQ3R7/xFR+uRWeffTaysrLQ2tqqWq8+cuRI+SFKdO+992L9+vVJ0/3T09Pl7WjAo6amBj/99BMAyN8gx7vxxhsTjq1duxb19fUwm83yQ1y8yy+/HFqtFjU1NUkf4JKxWCwYNWqU4n/Dhg2TAwO1tbX41a9+hXXr1ileZzabFYEd0RdffAGXy4XBgwfjxBNPPKT7ierfvz+OP/541fKDTz/9VA6SJLNs2TIEg0Hk5eUlXXf99dfDYDCgublZ9Rvj8847L+GYTqeTP+8ul+tQ3hKASN+GeOnp6fI1k/Xg6AznnHNOwrFoIKB///7o27dvwvn8/HwAgNvtVr3mlClTEo6lpaXJGUFffPHF4d5um/x+vzwpI9mf+fLychx//PGQJAlLliwBEOlnEM3qit6nyGg04qqrruqkuyYiOjL6tpcQEVFPlZeXByAyaeBIRFP3D/UBRvw2PVVWw969e3Hrrbdiz5490Gq1eOKJJxTf1LbF4XDIDwpqf5EfPXo0SktLUVFRgVmzZsnp7VGTJk3CypUrMX/+fNxxxx3y8WgDR/Ev+WKX/t/85jdJ7ykaXIk+KIii/1zUaDQaaLVarFmzBjt27EBFRQX27duHrVu3KjIeoqUH27dvhyRJMJlMcjAj3gknnJBwLPo+AoEArrvuuqT3o9PpEA6HsWvXLgwfPjzpunhDhw7FzJkzVc/t3r0bjzzyCFatWoWbbroJ8+bNU9z7pEmTMHfuXLk/RPRb8WjZweFmE0SNHz8eW7ZswWeffSb3OaiursbatWtx0kknobi4OOlro/88hwwZkjS7xmQyoX///ti2bRt2794tN9SMis+iiIoGgg6lgWdb14w2i4zPpOlMRUVFCceiQcZk/x6InlcLKObn56OwsFD1dccffzwAYM+ePYdzq+2yZ88eebzq448/nrTk5cCBAwBin5Ho/5vNZjlQEm/IkCEdfbtERB2CgQIiol4smtZaXV2N5ubmdtX7NzY2wuv1ok+fPvKx6F/GW1pasGfPnnY3GBS/hVZrwAZEUqJ/85vfwOFwwGAw4P/9v/+X8htdNfPnz5f/Iv/HP/4Rf/zjH5Ou/emnn7Bx40YMGzZMPnbhhRfCarViz549WL9+PYYPH44dO3Zg48aNMJlMuPjii+W1Yi10e1LE1Wqnk42WlCQJ//73v/HPf/4TtbW18nGNRoP+/fvjiiuuSPiWPRqMSTWJQq0/RfS+/H5/u97H4XzLnUz//v3xt7/9Deeeey6am5vxj3/8QzFl4+STT0ZZWRn27NmDhQsXYtKkSairq8OKFSug1+uTfqvbXuPHj8cLL7yAJUuWwO/3w2g0YsGCBZAkSW5+mUz0G++2/ixFf+dqpQdtZeYczpSCVONKj7ZoJpOaQyldiooGKtVEm64eaY+CVMRrR5uytmd99M+M2Bg2ntVqPcK7IyLqHAwUEBH1Yueffz6efvpphEIhfPvtt/IUglTef/99PP/88ygrK8P8+fNhNBpx+umnw2AwIBAI4PPPP1fU16cS7WyfnZ2tmir+6aef4v7774ff74fdbscrr7yCU0455dDeJCLTDoDIX8hTPcDV1tZCkiS8++67igfTtLQ0XHrppXj33Xcxb948DB8+XH4gv+SSSxQP4dG/9Nvtdnz33XeHfK+p/PWvf5X7Blx66aU466yzMHDgQBx33HEwm83Ys2dPQqAg+lCWLGUbUH9Yjb5u2LBhCSPgjobMzEyMGTMGX3zxherD18SJE/H8889j/vz5mDRpEj755BOEQiGcf/75it4Xh6Nfv34YOnQoNm3ahOXLl+P888/Hp59+Cp1O12YmS/Sz0NaDafQhsT2jRCk1r9eb9Fz0n4NapkKygEtLS8sh/XzxQf+HH35o9z/TaIBD7c9fVLIxnUREXY09CoiIerHS0lJ5TOA///nPNr+p9Pv9eO+99wBERtVFU2yzsrLkRlz//ve/5SaJqezZswcff/wxgEjtfPw3iZ988gmmT58Ov9+PPn36YNasWYcVJNiyZYvcM+Dpp5/GsmXLkv4v2tTv448/TniwjpYjLFq0COFwWO5lEJ/mHs3ScDgcqKurS3pfa9aswc6dO9ud8h0IBPDPf/4TQKSk4YUXXsDPf/5znHjiifKDSXV1dcLrorPfW1pasG/fPtVrR6dWqL2PPXv2JG1UKEkSvv32W0XqdUeKfibUPpdXXnkldDodVq9eDYfDgcWLFwM48rKDqPHjxwOINDCsqKjA+vXrMWbMGOTm5qZ83XHHHQcA2Lx5s2LyhMjtdsup8IfSZ4PUVVVVJQ2ERf/si9NYos1Fk31mxWyd9igtLZWvKTYqjLd+/Xps3bpVDgxE/4x5vV7s3r1b9TViKRMRUXfCQAERUS/34IMPQqPRYO3atXIH7mSee+45VFZWQqvV4vbbb1ecu//++5GXl4fa2lrcc889KR+AGxsbcdddd8Hn82Hw4MHy6LeodevW4f7770c4HMbgwYPx7rvvyg9gh+q///0vgEgwQ61JnOjaa68FEPmLe/w388OHD8egQYNQU1ODt99+G/v370ffvn1x8sknK9YNGDBAfvh76623VH/O999/j+uuuw6XXnopfvzxx3a9j6amJvmbU7EsQiSOKYw+3JeWlsqlIdHfRbzZs2cnHDvllFOQmZkJj8eTNKNg/vz5mDJlCsaPH68apDgSHo9HHoWnlm1SUFCAM888E8FgEHPnzsXatWuRk5Oj2ijvcEQDBUuWLJEDWj/72c/afN1ZZ50FvV6Puro6fPrpp6pr3nrrLQSDQWRkZMgjLY9EtPHl4ZQk9AahUAgffPBBwvGWlhZF5k9UVlYWgMjUF3E6SFQ06KRG7XdtsVjkf47/+c9/VF9XUVGByZMn4/LLL8dnn30GAOjTp4/8Z1ltKkM4HJazoYiIuhsGCoiIermTTjoJt912GwDgL3/5C6ZPn57wLVZlZSXuvfdeee77b37zm4SHN7vdjhdeeAGZmZlYsWIFfvnLX2L58uWKxmt+vx+ffvoprrrqKmzduhXZ2dl4/vnnFfXToVAI9913HwKBAHJycvDGG2+kbO6Xit/vlycVXHbZZUmbjEWdddZZclOx6Ig7UfTb6hdeeAFA5FtttekE0fF3//jHP/D6668rvrlcs2aNfP6kk06SJyq0JTs7W05VnjFjhiJro7GxEY899pj8QAsom9PdeeedACJZI++99578kBMIBPDyyy8nTHoAIunU0QDO//3f/2HOnDmKb8g///xzPProowAiD9VqneoPV0NDA+699144HA7o9fqkzRSjWR4vv/wyQqEQLr/8csUkjSNRWlqKYcOGobm5Ga+//joMBkO7SnOKiorwy1/+EgDw8MMPyw+FQOTB75133pHLR26//fZ29QVpSzSjJNosryM0NjZi586d2LlzZ4ddszM999xzigf8aDDywIED6N+/vyLTZMSIETAYDJAkCU899ZT8ZyUQCODf//63nDWlJlpmEP+7vvPOO6HT6fDxxx/j6aefVpQTbNu2DbfeeisCgQBKSkoUYxDvueceAMDMmTMxY8YM+c9YS0sLHn74YXliCRFRd8MeBUREx4Df/e53sNvt+POf/4yPP/4YH3/8MfLy8lBYWAiXyyV30zcYDPjtb3+LW265RfU6p5xyCv773//izjvvxNatW/GrX/0KNpsNffr0QTAYREVFhfyt+JgxY/Dcc8/JY8+iFi9eLKdl6/V63H333Snv/eGHH8bQoUNVz33++efyRIf4SQZqtFotrr76ajz//PPYtm0bvv/+e4wePVo+f8UVV+C5556Dx+OBRqNRHWkGRL553rNnD15++WU8++yzeO2111BWVobGxkbs378fQKxhX3vp9Xr89re/xeOPP45Vq1bh7LPPRllZGfx+P/bu3YtgMIihQ4eiqqoKTU1NqK6ulr+tvOCCC/CrX/0Kb7zxBh5++GG89NJLKCoqwt69e+F0OjFixAisW7dOTp+OuuWWW1BRUYH33nsPDz74IP785z+jT58+qKmpkdOzR40albI5ZDKbNm2SMziiJEmCx+PBzp07EQqFoNfr8eijj8oZEfHOPfdcZGdny6P9OqrsIGr8+PHYuHEjPB4PzjnnnJRN80QPPPAAampq8MUXX+C3v/2t3JW/oqJCbi55/fXXJ/1zdKiinfF/+OEHXHLJJRg4cCBeeeWVI7rm22+/LV9j69atR3yPna20tBR33HEHSkpKYLfbsX37dvj9fhQXF+Oll15SBAltNhumTZuGv//97/j444+xfPly9OnTB/v374fD4cC1116LL7/8EjU1NQk/Z+jQoVi9ejWeeOIJzJo1C5MnT8ZVV12F0aNH48knn8Sjjz6KGTNm4N1338WAAQPg8Xiwd+9eSJKE3Nxc/POf/1TcyxlnnIF7770Xzz33HJ5++mm8/vrrKCoqwq5du+DxeHDhhRemzHAgIuoqDBQQER0jpk6dinPPPRfvvfceVq1ahb1792LTpk1IT0/HkCFDMHbsWFx77bVtfnNcVlaGDz/8EAsXLsRnn32Gn376Cdu2bYNGo0FRURFGjhyJCRMm4Mwzz1R9fTTdHABqampU/7IuStU0LpoyP2zYsKQPm/GuuuoqvPzyywgEAnj33XcVgYKcnBycddZZ+PLLL3HaaaelHJP3m9/8BmeccQZmzpyJNWvWYMuWLTAYDBg6dCguvPBCTJky5ZAb2U2ePBn9+/fH66+/ju3bt2P79u2wWCwYMWIEfvazn+GXv/wlHnroIXz44YdYsmQJzj//fPm1//u//4uRI0firbfewsaNG7FlyxYMHDgQ9957LwYMGIDJkyfL4/eiNBoNnnzySVx88cV499138eOPP2Lz5s1IS0vDSSedhAkTJuDqq69uM1NDjdvtTpimoNPpYDabcfzxx+Pkk0/G1VdfjQEDBiS9hsFgwIQJE/Cf//wHJ5xwQtLJGYdr/PjxePbZZwHgkCZtGI1G/PWvf8WCBQswZ84cbNiwAZs3b0ZeXh7GjRuHq6++GqeeemqH3eeVV16JXbt24aOPPkJlZSUCgUDS/gi91cyZM/H3v/8dCxYswI4dO1BSUoJLLrkEU6ZMkUsNRL/73e8wcOBAzJo1C5s3b8bu3btRXl4ulwd8+eWXqj/nqaeewmOPPYa1a9di9+7dirGLkyZNwkknnYR///vfWLlyJbZv3w6NRoMBAwbgnHPOwc0336zaaPOWW27BiBEj8Oabb+Knn37C9u3bMXDgQNx8880oKChgoICIuiWNdKwWvBERER0jli5dittuuw1lZWVYuHBhV9/OIbnzzjuxaNEiPPLII0lLFOjwLFy4EPfddx/WrVvX1bei6rvvvsONN94IANi4cWOHlZ0QEVHb2KOAiIioh4t+879x40bV81999RUAJC3h6K7q6uqwZMkSZGRk4PLLL+/q2+l1Vq1alTKjg4iIjl0MFBAREfVwZWVl+PHHH/HMM88oRr8Fg0HMnj0bs2fPhkajSegZ0B3V1NRg79692Lx5M+68804EAgFMmjSpQ5oCUswnn3yC2bNny403iYiIRMzhIiIi6uGmT5+O77//HqtWrcJ5552Hvn37Ij09XW7eptVqcd9993XIqL7Otnr1akyfPl3ez8vLwx133NGFd9Q7nXbaaZgzZw7Ky8u7+laIiKgbYqCAiIioh+vfvz8+/fRTzJo1C59//jn279+PlpYW5OXl4ZxzzsHkyZMxYsSIrr7NdhkwYABKSkrQ2NiIk046CY888ohqszo6Mjk5OaqN94iIiAA2MyQiIiIiIiIiAXsUEBEREREREZGMgQIiIiIiIiIikjFQQEREREREREQyNjPsQpIkIRgMd/VtdCmDQQcACARCXXwn1J3xc0Jt4WeE2oOfE2oPfk6oLfyMUHt0x8+JXq+FRqNp39pOvhdKIRgMw+HwdvVtdKm8vMhc7GP990Cp8XNCbeFnhNqDnxNqD35OqC38jFB7dMfPid1ukgMYbWHpARERERERERHJGCggIiIiIiIiIhkDBUREREREREQkY6CAiIiIiIiIiGQMFBARERERERGRjIECIiIiIiIiIpIxUEBEREREREREMgYKiIiIiIiIiEjGQAERERERERERyRgoICIiIiIiIiIZAwVEREREREREJGOggIiIiIiIiIhkDBQQERERERERkYyBAiIiIiIiIiKSMVBARERERERERDIGCoiIiIiIiIhIxkABEREREREREckYKCAiIiIiIiIiGQMFRERERERERCRjoICIiIiIiIiIZAwUEBEREREREZGMgQIiIiIiIiIikjFQQEREREREREQyBgqIiIiIiIiISMZAARERERERERHJGCggIiIiIiIiIhkDBUREREREREQkY6CAiIiIiIiIqJ0+WLYL0//6NRavqejqW+k0DBQQERERERERtUNTsw/zV+5BU7MPsz7fDm9rsKtvqVMwUEBERERERETUDlUNHsX+jv2OrrmRTsZAAREREREREVE71DtbFftb9jm65kY6GQMFRERERERERO1Q0+RV7G/d19RFd9K5GCggIiIiIiIiaoe6phbF/p7qZrT4el+fAgYKiIiIiIiIiNqhNi5QIEmJx3oDBgqIiIiIiIiI2iBJEmodiUEBl9ffBXfTuRgoICIiIiIiImpDc0sArf5QwnGXh4ECIiIiIiIiomNOshKDZm/gKN9J52OggIiIiIiIiKgN8Y0Mo5hRQERERERERHQMih+NGMUeBURERERERETHoDqhkWFGmk7eZqCAiIiIiIiI6Bgk9igYUGyTt1l6QERERERERHQMEkcjDiyJBQrYzJCIiIiIiIjoGNPiCyoCAgP6KDMKJEnqitvqNAwUEBEREREREaUglh0Y9FqU5lvk/VBYgtcX7Irb6jQMFBARERERERGlsLvKJW/n2zNgyTBAq9HIx3pbnwIGCoiIiIiIiIiSqGrw4N0vt8v7fQss0Go0yDQZ5GO9rU8BAwVERERERERESSxZux/+QBhAZCzihHFlAACr2SivcTKjgIiIiIiIiOjYIJYdXHHGcSjKMQMAbJZYoMDh9h31++pM+q6+ASIiIiIiIqKjbfPeJny4fBeO75uFK8/sD43QcyAqFA6josYt7w8oscrbdkuavM1AAREREREREVEP99airahq8GJ7pRMDSqwYPiA3YU1VvRf+YKTsQKfVoDQvNu1AEShoZukBERERERERUY/lbQ2iqsEr77+/ZKfqut3VsbKD4lwzjAadvJ+V2XszChgoICIiIiIiomNKZZ1bsb+/3oN6R0vCuj3VzfJ2WWGm4py9F/coYKCAiIiIiIiIjin7apoTjm3a25RwrKYxlnXQtyA+UCBmFLD0gIiIiIiIiKjHqqh1JxxTywpoao4dy7amKc6JgYIWXxA+f6gD77BrMVBARERERERExxS1QIHLk5gVIAYKxJ4EAGA1GyAOSnB4ek/5AQMFREREREREdMwIhsLYX+9JOB4fKGjxBdEqZAlkWZSBAp1WC6tZ6FPQzEABERERERERUY9TWedG4ODIQ1F8oEDMJtBpNcgUggJRvbVPAQMFRERERERE1KPVNHnbPXlgR6VT9bjTG1DsNwnXs1uM0Ip1BgeJWQbe1kDC+Z6KgQIiIiIiIiLqsVZtrsEDr32L//3bSuytTpxmEG/H/ligYHAfm7wdn1EglhLY4/oTRI07oRAaDZBu1GFw36xDvfVuS9/VN0BERERERER0uN74eBMAIBSWMO/r3bhz0vCU63fud8nbowbnYdvBDIMWXxCBYAgGvQ5AXCNDi3qg4OTj8/H/isbBaNAi05RYmtBTMaOAiIiIiIiIeiR3SwDBkCTvr91en3K9w+1Dg6tV3h85OE9x3uWJlQ80tSOjAABybOm9KkgAMFBAREREREREPdSmPY2KfQ2AVn8w6XpxLKLdYkSePQOmtFiivcsbKz9INRqxt2OggIiIiIiIiHqkn3Y1KPYlALsPuNQXA9hfFxuL2CfPAgCKEYdOoU+B2MyQgQIiIiIiIiKiHmBvtTvh2Pb96lMNAGB/fWx9ca4ZgDJQEG1oKEkSahq98vFca8YR32tPwkABERERERER9UhqIxHFrIFU50rykgcKGpytaPWH5OPRoMKxgoECIiIiIiIi6nECwTDcLYGE400qwQMACEsSDjQIgYLcSOmBTSw9cEcCBZVCQCHHmgZT+rE1MJCBAiIiIiIiIupxnEkCAo5m9eP1zlb4A2F5vzjXBEDZfyAaZKisi5UolBzsZXAsYaCAiIiIiIiIehyH25/kuA+SJCUcFyck5NrSkW6MZAkoAgXNkdGJYqCgDwMFRERERERERN2fWGKQY02Xt4MhCc1xJQmBYBifrNwj7w/rny1vZ1nEQEHkmsrpCMdWfwKAgQIiIiIiIiLqJsJhCf5AqO2FUJYYFOeaYTRoVc8BwJottWhwRY7ptBr87LR+8rksayxQ4PT4EQyFUS1MPGBGAREREREREVEXqHW04PevfYO7/rIca7fVtblenHiQlWlUZAY0xgUKdlW55O0xQ/KRa4+NOxRfJ0mRbIJQOFa6kGOLZSscKxgoICIiIiIioi4VDkv4y/vrIg0Hg2EsXLWvzdeIpQd2S5qi10B8RsGB+lgpQVmhVXHOaNDBLEw12FMdCyoY9FqkG3XtfyO9BAMFRERERERE1KW+3VSNqoZYuv+2SidC4XCKVyiDAfGBgqa4QEGVMBax6OC0A5H42r3VzfK21WSERqNpxzvoXRgoICIiIiIioi61c78r4VhVvVdlZYw49cCemQa7yphDAPC2BhRri3MSmxNmZcbKC/YIgQKbxdjGnfdODBQQERERERFRl3LHTSkAgN1VicGDqGAojAZXq7yfZUlT9BoQsw0OCJkK6UadIntAfn1mLCAQn1FwLGKggIiIiIiIiLqUp/XQAgV7qpsRCEZKE/Q6LYpzTcoeBUJGQZXQn6Aox6xaSiBmFEjCcWYUEBEREREREXUB1YwC4Zv9eNsqHPL2gGIrDHodbOZYoMDliZUaHBD6ExSr9CcAgGyVLAOAGQVEREREREREXcLTEkw4Ft+QULR1n0PeHlxqBwBYhW//m70BuRlivSNWolCYrR4oyE0yApEZBURERERERERdwK1SeuD2BiBJUsJxnz+EHfsd8n55XzsAwCZ8+y8hEiwAgEYh4JCdqR4QyLFnqB5nRgERERERERHRURYMheHzhxKOhyUJLb7ETIMPV+xCiy+y3qDXYkCxDQCQZtQhzaiT1zkPTjpoahaaHiYpMcjOTIPaFERmFBAREREREREdZR6V/gRR8b0Lmpp9WLS6Qt6/6JRSRXDAZo492Ds9fgRDYTlgAABZVvVAgV6nVQ0iWM0MFBAREREREREdVe7WWNaAXqeFJcMg7zfHBQoqapsRrUawmo24bFyZ4rwyUOCDy+NXTDEQRyjGy7UmliWw9ICIiIiIiIjoKBMzCiwZekWgwO1VBgrEfgPFOSYYDTrFeTFQ4PL4FestGYaE9aIcm7JPgdVsRLox+frejIECIiIiIiIi6jJioMCcYYDFJAQK4jIKHMKDv1qpgDgi0enxKyYnJOtPEBU/+eC0oQXQqDUuOAbou/oGiIiIiIiI6NglBgMs6QaY0mOPqc0pMgqyVCYYiCMSXR4/mlxtNzKUf7aQyQAAZwwvauPOey8GCoiIiIiIiKjLeIQeBeYMA8xCoMDTmtjMMEo9o0DoUeD2x41GTB0oGFKWJW8P7GNDnzxLO+6+d2KggIiIiIiIiLqMO65HgVlsZug9tECBNW7qwaGUHvTJs+DWy4Zid1UzLjqltP1voBdioICIiIiIiIi6jBgoMKcbkJlhVD0HAE3NqUsJ4qcemJv1wvrEUoV4pw0rxGnDCtt3470YmxkSERERERFRl1FOPTDETT3wy9stviBafCF5X62UwC6MP2zxhbC/ziPv59jaDhRQBDMKiIiIiIiIqMvsrWmWt7OsaUg3CM0MhSCCWEag02qQKWQPRNnMRmg1GoQlCQDQ6o8FFuKnGlByzCggIiIiIiKiLtHoakW9M1ZOMLiPPel4xCZ3LFBgt6RBqzK6UKvVwGZJDCBoNG33KKAYBgqIiIiIiIioS2zd55C38+zpyLamI1MoPfC0BOXsgHpHi3w81UO/WH4QlZ2ZBr2Oj7/txd8UERERERERdYkt+5rk7fLSyHjCTCGjICxJ8B4cn7i3OlaiUJJnTnpNtSBCjpVlB4eCgQIiIiIiIiLqUPNX7sHj/1qN77fWpVy384BL3i7vawcAZKTpodPGygpcnkhDw91CoKCsMDPpNe0qpQc5tox23TdFMFBAREREREREHWZfTTM+WLYLe2uaMXPhFkgHSwfiSZKkKCcozbcAADQaDaxCo8Jmrx+BYBiVtW75WP8ia9Kfr5ZRwEaGh4aBAiIiIiIiIuowq7fUytsubwAOt191nbslAH8wLO9nC+UBYvmByxtARa0boXAk4GDQa1Gcm7z0QK1HAQMFh4bjEYmIiIiIiKjD7K5yKfarGjyq3/I3uGLTDtIMOpjTY4+nVlMso8Dl8aPZGws29M23pGxMqNqjgIGCQ8KMAiIiIiIiIuoQ/kAI2yqcimNVDV7VtQ3O2LjDHFs6NMK4w0yTsvSgujF2jdKC5P0JgMRAgTldjwEltrZvnmQMFBAREREREVGH2LnfiWAorDh2oMGjurZRyCjItiof7m1CjwKXNyA3NASALJVmhaLszHRFxsHNlw5BmkHX9s2TjIECIiIiIiIi6hA1QnPCqKp69UCBWHqQGze+MNMs9Cjw+OEU+hzYVHoQiNKMOky+cBD6Flhw3YWDMXJwXrvunWLYo4CIiIiIiIg6hKPZl3CsqjFJ6YEio0AZKFD0KPD64fYG5H0x2yCZc04qwTknlbS5jtQxo4CIiIiIiIg6RKNKoMDp9sPbGkxcKwQKcuIzCsQeBR4/nB4xo6DtQAEdGQYKiIiIiIiIqEOoZRQAQL0zsSShwSkECuKmEliF0oMGVytafLFAg82cuvSAjhwDBURERERERNQhmpIGCloV+w63Dy6hnCA3PlAgZBQEQ5LiXKbJAOpcDBQQERERERFRhxADBZaM2AN9fKBg854meTvHmp4w0jBZMMCSYVBMNKDOwd8wERERERERHTGfPwSvUCIwqI9N3o4vPdi0p1HeHlqWBY1Gozhv0OtgTk/svW9nf4KjgoECIiIiIiIiUuVtDeDbjdWoUxl7GK/JHcsm0Go0OK7YKu/XO2IZBZIkYdPeWEbBkLIs1esVZpsSjrVn4gEdOY5HJCIiIiIiogSSJOGlOT9hW4UDmSYD/u+W0xTlBPGahCkGNosR+VmxB32x9KDB2aooURjSL1v1eoXZJuw84FIcs7KR4VHBjAIiIiIiIiJKsGlPE7ZVOAAAzd4AVm+pTblezCjIykxTNChscLVAkqSD20JAwWxMmiVQmKOSUcDSg6OCgQIiIiIiIiJKsOC7vYr97zbVpFzf4BICBZY0xcjDFl+sf4EYULBnJs8QUCs9yE6xnjoOAwVERERERESk4G4JYJMwmQAAtlU40OhqTfIKoLbJK2/nZWUgM8MAoyH2yBntU+Bo9svHsiyHFig4cUBO2zdPR6zHBgruuusulJeXY+7cuarnV65cialTp2Ls2LEYOXIkJk2ahPfff19Od4kXDAYxe/ZsTJw4EaNGjcKYMWMwbdo0fPfdd535NoiIiIiIiLqdBqd6QGDHfmfS19Q0xhoeFmaboNFokJUZyypweiKZBI52ZhTkZ2Uo9k1pehRkJQYPqOP1yEDB+++/j4ULFyY9P2vWLEydOhWrV6/G0KFDceqpp2Lnzp146KGH8OCDDyaslyQJ999/Px555BFUVlZi3LhxGDx4MFauXIkpU6Zgzpw5nfl2iIiIiIiIuhWx2aDI6fGrHgeAGiGjoODgQ36m0Pyw2RtIuHaqcYcGvQ5aYWziWSOK27hr6ig9burB7t278dRTT6U8/+STT8JisWDmzJkYOnQoAODAgQOYMmUK5s6di7PPPhuXXHKJ/Jo5c+bg448/xpAhQzBjxgzY7XYAwDfffIPbbrsNjz/+OMaNG4eioqJOfW9ERERERETdQVOzekaBK0mgwNsakAMBAFBwsGxAnJLgbomcFzMKUpUeAMAvzh2A2V/uQK4tHZeO7de+m6cj1qMyCvx+P6ZPnw6tVisHAOK98cYbCIVCmDZtmmJNcXExHnnkEXmN6LXXXgMAPPTQQ3KQAADGjh2LKVOmwOfzYebMmR38boiIiIiIiLqnxiQZBckCBTVNsbKDNKNOnmRgMSUGCsSMgqw2mhNePKYvnvvN6Xjq1tSjGalj9ahAwQsvvICNGzfikUceSfrt/pIlSwAAF110UcK5cePGITMzEz/99BNqaiIdO3fs2IF9+/YhJycHo0ePTnjNxRdfDAD44osvOuptEBERERERdWuNwgQDsSGhmDUgqm5Ulh1oDpYMZMZlFEiSBIc7Fmywt5FRAESCCXpdj3p07fF6zG975cqV+Ne//oWf/exnuOKKK1TX1NfXo6GhAQaDAccdd1zCeZ1OJx/funUrAGDbtm0AgEGDBskfZlH0+L59+9DS0pJwnoiIiIiIqLcRSw/KCjLl7WQ9CmqEQIE4rUCRUeANwNMaRDAUlo+lamZIXadHBAoaGxtx3333obCwEI899ljSdbW1tQCA3NxcaLXqby0/P1+xNvr/BQUFquvT0tJgtVoRDodRX19/uG+BiIiIiIioxxBLD/oVWuXtZm9ioKDFF8RX6w7I+0U5ZnlbLBdobgkoyg70Oi3M6T2ubd4xoUf8U/nDH/6AhoYGzJgxA1arNek6rzcSxUpPT0+6Ji0tTbHW4/G0+zXRtR3FYNAhLy+z7YXHAP4eqD34OaG28DNC7cHPCbUHPyfUlp74GQmFwtBqNaqZ1CJJkuAQHuhPHJyHxWsqAAAubwC5uRbFNf79ySY4D5YTGPRaTDhrAPIOBgv6FNrkdS3+ECShhCDXno78/OTPd71BT/ycAD0go+Dtt9/Gl19+iWnTpuHUU09NuTaaRdDWBx+IfPiBSDlCe19DRERERETUE63fUYcbHluIO59dIjcVTKbZG4A/GCsPGNjHLm/7AyG0+kOK9as2VcvbV503CIVCRoHVHBt/2Ozxo8EZK2nIsWUc8vugo6NbZxRs374d/+///T8MGzYMv/3tb9tcbzZHPpCtreqjPADA54tExkwm0yG/JiOjYz/IgUAIDoe37YW9WDTCVlfX3MV3Qt0ZPyfUFn5GqD34OaH24OeE2tJTPyN/nrkGzV4/mr1+/HveBvzyvIFJ1+6rib03o14LA8LQAJAOHtu1twH5WZHnqWAojP21bnn9kFKb4ncT9MWCEi6PH/sOOOV9c5qux/0e26s7fk7sdhMMBl271nbrQMGzzz6L1tZWpKen44EHHlCc27hxIwDgvffew8qVK3HKKafIEwrq6+shSZJqlkC0J0G0V0G0N0FdXZ3qPfh8PrhcLmg0GuTl5XXMGyMiIiIiIjqKxEkD322uSRkoEPsTZFnTodNqYTEZ5IkHLk8A+VmR81UNXoTCB7O1tRpFI0NA2cwwLEk4UB8r527PxAPqGt06UBDtI/D999/j+++/V12zdu1arF27Fnq9HldffTUKCgpQU1ODvXv3oqysTLE2FAph165dAIDy8nLF/2/fvl31+tu3b4ckSejbt6+chUBERERERNRThMJhxX44LCVZGSE2HMw+OJXAajLGAgVCQ8P9dbFsgsIcU8IYw4w0PbQaDcIHS78rhfUMFHRf3bpHwcyZM7F161bV/51//vkAgKeffhpbt27FM888AwA4++yzAQCLFi1KuN7XX3+N5uZmHH/88SgsLAQA9OvXD2VlZaitrcWPP/6Y8JrPPvsMAHDOOed0wjskIiIiIiLqXI0un2JfHE+oRhyNmBUNFAi9BlzCiMTKuliGQJ88S8K1tBoNzBmx76erGmKl11kcjdhtdetAweG47rrroNPp8Nprryke/A8cOIAnn3wSAHDbbbcpXnPjjTcCAB5++GHFCMRvvvkG//nPf2AwGHDzzTd3/s0TERERERF1sFpHi2Lf0xpEqz+YdL0YWMi2qgQKvGKgIJYh0Ccv1sRQJI5IFDFQ0H1169KDw3H88cfj7rvvxnPPPYfJkydjzJgxSE9Px3fffQev14tf/OIXuPTSSxWvufbaa7Fs2TIsXboUF198MU499VQ0NzdjzZo1kCQJzzzzDIqKirroHRERERERER2+urhAAQDUNLagX6H66D6x9CArMzJGPlPoNSBmFOwXMgpKVDIKACAzw4AqleN2i1HlKHUHvS5QAAC33norBgwYgBkzZmD9+vXQaDQYMGAArr32Wvz85z9PWK/VavHKK6/grbfewty5c7FixQpYLBaMGzcOt912G8aMGdMF74KIiIiIiOjI1TUlBgqqGj1JAwWKZoYHv/W3qZQehMJhNAplCvGNDKOybelApTPhOHsUdF89NlDwt7/9LeX5888/X+5j0B4GgwFTp07F1KlTj/TWiIiIiIiIuo1kGQVqJElCkyv28B9tZphpEksPIk0NnW4/JKEvYrJSgnx74ph5c7oexnaO6qOjr9f1KCAiIiIiIqKY+B4FAFDd6FVZGelf4A/Gmh1mWyOlB2rNDMVeBpYMA9KSPPgXqGQa2NmfoFtjoICIiIiIiKiXCobCOFCfGBSoblAPFIj9CQx6LczpkSR0sfSg+WAzQ7HsIDvFg39+VmJGQRbLDro1BgqIiIiIiIh6qeoGr+o4xOomLySxbuCgDbsa5O2szDRoNBoAymaGntYggqFw3HSE9KT3UJCVmFGQrD8CdQ8MFBAREREREfVSe2ua5e0ca+xbfJ8/BIfbr1h7oN6Duct2yfviA77VpJxQ0OwNoFHoZZBlTZ4hYMkwQK/TKI6deFxOO98BdQUGCoiIiIiIiHqpfTVuebu8b5ai10B8n4Kla/cjFI5kGWgA/GxsP/mc0aBDujHWg8Dl8SumI6QqPQCAYEiZvXBcsbX9b4KOOgYKiIiIiIiIeqmK2lhGQd+CTBQK/QJq4gIF+2pjQYXxp/XD4FK74rwYZHB6/IqMgpwUpQcA0CfPLG+b0/XQ6/go2p3xnw4REREREVEvFA5L2CtkFPQrsKAwJ1ZOIGYUSJKECiFQMHxAYmmAWH7Q7I3LKGgjUPDzs46Tt2+/8oR2vgPqKvquvgEiIiIiIiLqeLurXGjxBQEAWo0GfQsysbsqlmEgZhQ0OFvltQDQJ8+ScD0xo6DB1SqPSQTaLj0YOSgPT04bA51Oi0KVcYnUvTBQQERERERE1Aut2xmbYDCojw0ZaXpkCQ/0TuFBXyw7yLWlw5Se+KgoBgp2HXDJ21qNBvY2AgUAUKISfKDuiaUHREREREREPUijqxVVDZ42163fWS9vjxiYC0D5sN/sjQUKxLKD0nz1B3qrMCJx6z6HvJ1nT2fPgV6GGQVEREREREQ9xJ5qF/7vP98jFJZw62VDcdqwQtV1To9fMfEg2nNAfNh3egKQJAkajQb769oOFIjZCL5ASN4uYClBr8OwDxERERERUQ/xr0+3yCMMP1i+K+m6A8KDv9VkQNHBJoaZQkZBMBRGqz/ywN/gijUmLMhSf/BP1rCQPQd6HwYKiIiIiIiIeoBAMKwoEahztCZdK040KMwxQ6PRAAAsGQYc3AQAuSFhU3PsWllJ+g0ka1jIjILeh4ECIiIiIiKiHmDjnsaEYy6hz4CoSgwUCA/yWo0GmcKYQ5fXj2AoDKc7dp0sq3pAICtTPaOgICsj9Y1Tj8NAARERERERUQ/w066GhGP7hQwDUXWSQAGg7FPg8gTgdPshCeeTZQ6Y0vVIN+oSjrP0oPdhoICIiIiIiKgHaHAmlhpU1KlPP6hJESiIzyhoao71J7BkGGDQJwYDouL7FBj12naNRqSehYECIiIiIiKiHkB8oI+qVMkoCATDqBeCCoU5ykCBTRyR6PGjUehPkCybICq+f8FxxVZoxaYH1CtwPCIREREREVEPoBooqEsMFNQ2eSEdrCXQaTXItSmzAMSMAqfXjzShnCBZI8Oo+EDCiQfHLlLvwkABERERERFRNxcIhuFuCSQcr3O0JBwT+xPk2TOg1ykTya3mWI+CZo8fem3sfFaSEYhRurhrDT+OgYLeiIECIiIiIiKibs7hTswmAABPaxDulgAsGbGH/1SNDAHAquhRoAw+tJVRED/hoDjXnHI99UzsUUBERERERNTNiWUHmSaDYvpAfFZBW4GCTKFHgdOjbGbYVo+C008sQubBqQnXXzQYGvYn6JWYUUBERERERNTNiRkFWZbIw/y+g40Ma5q86F9klc8rAgU5iYECuyUWKHC4fWj1B+X9tgIFlgwD/t//jIOnJZAwAYF6DwYKiIiIiIiIujmH8K2/PTMNRr1WDhTUNSkzCmoaY/vxpQJALNAAAD5/CD5/SN7PtSeuj5dm0CHNkHyEIvV8DBQQERERERF1c01iRkFmGkzpsUe5WiFQ4G4JKJoeFuYk9hDINBuh02oQCkuK41qNBtnW1BkFdGxgjwIiIiIiIqJuTuwjkGVJQ0FWrKSgVuhRUN0QKzvISNPDaoo1OYzSajSwCeUH8nUz06DT8hGRGCggIiIiIiLqEjsqnVjw3V44Pf4214p9B+yZacgTSgTEjIKqRo+8XZhtStpsUCw/iMqzs+cARbD0gIiIiIiI6ChrdLXi/81ai2AojB+31+P3141K+lBf62jBvhq3vH9ckRVpwtQDl8ePYCgMvU6rCBoUZCfvN2BXaVqYY2OggCKYUUBERERERHSUbdzdiGAoDADYXunE7qrmpGtXb66Rt4tzzSjJM8MqjDiUADR7I30JxFGJ+SkaE6plFOTa2m5kSMcGBgqIiIiIiIiOsoo6t2J/yQ+VSdeu3lIrb48Zkg+NRoM0gw4ZacqsAkBZhpCXKlCgklGQy4wCOoiBAiIiIiIioqNsX7Uyg2D1llo5w0Dk8voVZQcnl+fL21Zz7GHf6Yk0O1RkFKiMRoxSKz1goICiGCggIiIiIiI6isKShL21yowCfzCseMiP2rbPIW/bLEYU5cSmHdiE8gOn2w9vawCe1qB87FBKD/Q6LfrkW9r9Hqh3Y6CAiIiIiIjoKKpp9MLnDyUcF0cbRm0VAgXH981SNDxUBAo8fsWYxDSDTtHHIF52XPbAz8/qD3N64ihFOjYxUEBERERERHQEJElCg7MVkiS1a/3uKpfqcXEEYtSWiiZ5u7yvXXEuIVCg6E+QnnSKAgDk2dIxfEAOAGDssAJcPKZvu+6djg0cj0hERERERHQE3vh4M77ZWI0h/bJw7zUnpXxAB4ANuxtVj1fFBQq8rUHsr/PI++WldsV5m0UZKBBLF1I1MgQAjUaDu64aDndLAJkZhjbvmY4tzCggIiIiIiI6TDVNXnyzsRoAsHlvk6LxoJpwWMKGXbFAwbD+2fJ2fEZBTVNs32jQojDbpDgvlha43D7UO1vl/bYCBQCg1WhgNRkZJKAEDBQQEREREREdplWbaxX7ycoK5PPVLrhbAgAAnVaD80aVyOfiexSIgYPCLFPCA71NmHrg8PjhdPvlfbXxh0TtxUABERERERHRYVq1uUaxv/OAM+V6MZtgUB8b+hdZ5X13S0AOIgCRpodRBXHZBEBij4LoiMT4c0SHioECIiIiIiKiw1DvbFH0EACAXQdSZxRs2RtrTjisfzZsZiPSjDrFNaOq2woUCD0KfP6QopmhzcKMAjp8DBQQEREREREdhiqVcYZVDV54WgMqq4FAMISdQiAhOu7QZoo98Lu9YkZB7MG/MDux50CmyQCdNlaO4GkNytvMKKAjwUABERERERHRYahRGWcIIGlDw537XQiGwgCANIMO/QozAQAWk0Fe03yw9ECSJFQ3pc4o0Gm1yLWlq/4su4WBAjp8DBQQEREREREdhhoh1V/kaPapHt+yL1Z2MLCPDXpd5HHMkhELFEQzCpweP3z+kHy8ICsxUACoBxD0Oi0y0vRt3D1RcgwUEBERERERHYZkGQUOj3qg4Mcd9fL28X3t8nZmRmJGgdhvwJJhUAQTRPlZiSUJdgtHHtKRYaCAiIiIiIjoMNQIpQHZ1ljzQHFMYVRVg0dRkjByUJ68LZYeRKceNLpa5WM5ScoLAPVMAxvLDugIMVBARERERER0iIKhMOqdsYf5waV2edvhTswo+G5TbIxi33wLinPN8r6y9CASZGgQAwXWFIEClSaHNjMnHtCRYaCAiIiIiIjoENU5WiBJkW2NBhhYYpPPqWUUrNvZIG+fOrRAcS5TnHogZxTEgg1itkI8ZhRQZ2CggIiIiIiI6BBVC/0Jcm3pim/9HZ7EQEGd0HNAzD4A4jIKDgYK2ptRkGNNh16n7EfA0Yh0pBgoICIiIiIiOkQVQr+BklwL7BaxR4Gy9MDbGoTXF5T340caWlSaGTa2M1Cg1WpwXLFNcSzZyESi9mKggIiIiIiI6BDtrWmWt/sWWBTp/q3+EFr9scCAmB2g12mRGfeNf/x4REmS0KAoPUj94H/LhKE448QiZFvTMKRflqJRItHh4HBNIiIiIiKiQyROMOhXkAmryQiNBnLfAqfbj/TsyONWg1PMDkiDNm50oTj1IBSW0NTsQ4uQgZCTokcBEJmKcPPPhhz2eyGKx4wCIiIiIiKiQ9Ds9SuyBPoWZEKr1cAqNCUUJx80tDHq0Jyuhxg6ELMV9DpNQgYCUWdjoICIiIiIiAhAiy+IL76vxMbdjSnX7T7glLfN6Xp5KoFYfuAUGhoqMwoSAwU6rRam9Fiy997qWKAgOzM9IQOBqLMxUEBERERERARg1hfb8fbibXh+9o/YXeVKum73gdi5vgWZ0Bx8kBcbGjqEEYn1bWQUAIBFyEbYXRULFCRbT9SZGCggIiIiIqJjni8Qwor1VQAACcDCVfuSrq2u98jbRTkmeVscSyhOPmgrowAAsoRshA27G+TtgqyMdtw9UcdioICIiIiIiI55m/Yoyw32COn/8aobvfJ2nj32IG9LklEg9ihINrqwJNcib0cbIgJAfpZJZTVR52KggIiIiIiIjnk/bKtT7Nc2taBReMAXVTfEMgrEQIFd0aMgklHQ4gvCJfQryLWpZwiU5JtVjxdkM6OAjj4GCoiIiIiI6JgWliSs39mQcHyDSlPDcFhCTbKMAnMso8B5MKPggBBUSDPq5MaH8frkWVSPFzCjgLoAAwVERERERHRMO1DnQbM3kHC8stadcKypuRWBYFjeF0sJ7JmJ4xEP1MUCBcU5ZrnxYbyS3MSMAo1GGYggOloYKCAiIiIiomPa5r1NqsfrnYmlB9UNsWyCTJMBGWmxsYZ2IaPA0xpEIBhWZBQU5ybPDshI0yf0L8ixpsOg5yMbHX381BERERER0TFty75YoEB8WK93tiSsrapX708AADahRwEQ6VNwoD4WWChWyRoQxZcfcOIBdRUGCoiIiIiI6JgVDkvYss8h7585vEjernO2QhJHEACobkweKNDrtLBkGOR9p9uPA0JgQa28QHTeqBLodbHShAEltva9CaIOpm97CRERERERUc8RDIWxeHUFgqEwLjm1X8r0/QZXK1p8QXl/7AmF+GD5bgCAzx+CuyWATFMsU6CmQb2RYZTNYoS7JdLvoKbJqxiNWJyTOlBwwnE5ePrWsVi5sRqSJOGiU0rbeKdEnYOBAiIiIiIi6lVW/FSF95fuBABoNBpMGFeWdG2tI1ZekJWZhhxrOtKNOrT6QwAifQrEQIFyNKKypwAA2M1G7D/YwHB7pVM+btBrkW1LXB8vx5aOy1LcL9HRwNIDIiIiIiLqVd5fslPenrtsV8q1dU2xQEGePQMajQa5tlimQJ1D2aegWhiNmK+aURBraLhDCBTk2tKhTTLxgKi7YaCAiIiIiIh6FX8glHJfVCsECvIPNg8UMwXEyQc+fwiOZp+8r1Z6kJUZCxTsT9H4kKg7Y6CAiIiIiIh6lXSjTrG/u8qVdK1YehDNEBAzCuqF83XCFAS9TgO7kD0QlWNVLy+IH31I1J0xUEBERERERL1Giy8IT2tQcWxbhSPp+tomoZTgYEZBjjUWAGgUMgjEMoQcWwa02sRSgmQBATH4QNTdMVBARERERES9hlhKELXzgHpGgSRJioyCaHmA2GfA6fbL28p+BuoBgZwkgYJk64m6IwYKiIiIiIio14hvPggANSrBAwBwevzwB8LyfjSjwG4xCmvEjIJYv4JkPQeSlx4wo4B6DgYKiIiIiIio16gRSgmiGpwtCIelhOM798cyDSwZBpjTDQCUGQUuT0B+rdijIC/Jg7/RoIPVbEw4zowC6kkYKCAiIiIiol5DrfQgGJLQJPQaiFq1uUbeHtY/W962CQ/6YUlCc0sAgDJbIZp9oCY+q8CUpofpYBCCqCdgoICIiIiIiHqNmsbEjAIgsSTB5w9h3c56eX/MkHx5O92og9EQe1Ryun0IS1K7Sg+AxIaGZUWZ7bt5om6CgQIiIiIiIuo1DjS0L1CwaU+j3J8gI02PE/rnyOc0Gg3sZqGhoccPp9uPYCjWzyDVuMM0g3I840Wn9G3/GyDqBhgoICIiIiKibikUDmPrvib8uL1e8ZCeTLPXD/fBMgEAGFaWJW/XxgUKqoTMg/JSOwx65aORTWho6HD7FIEGm8WIjDR90vsYVGqTt3Os6TjxuOyka4m6o+SfbiIiIiIioi7idPvwp3fWovrgA/1ZI4pw0/ghKV9TJWQTmNP1GFBiw8Y9TQASMwrEXgZq/QbEPgVOtx9ajUbeL8w2p7yPMUMK8OP2ejjcPkwdPwQa4bVEPQEDBURERERE1O2s2lIrBwkAYMX6alxxxnHIykxL+pqqBo+8XZhjUvQREPsLRPaFCQYq/QbEyQdOjx+BYCyjoSDHlPLe0ww63DlpeMo1RN0ZSw+IiIiIiKjbaXQpH+zDkoTl6w6kfI2YUVCUY1b0EWhwHVqgwG4RMwp8itGIhTmpMwqIejoGCoiIiIiIqNtxuv0Jx1b8VJXyNRW1bnm7KMcEq1A+4PYGEJYkAEAwFFYEDtRKD8TXOjx+RWChMDt1RgFRT8dAARERERERdTsOty/hWL2zFb5ASHX9F99XYvPeJnm/T54FmabYw35YkuBtDQIAGpytOBgzgAaRhoPxsoVjDc5W1AulC8wooN6OgQIiIiIiIup2nJ7EjAIgsSQBAKobvZj95XZ5v2+BBUPLsmBK1yuaEEYnIojZAdnWtISJB4CyHKGp2ae4nzyVDASi3oSBAiIiIiIi6nYcKqUHANDoSsw0eHvRVgRDkRQBu8WIu38xAjqtFlqNBhaTQV7X7I1cs7aN/gQAkGNNg06bOK1AowFybAwUUO/GQAEREREREXUr/kAILb6gvJ9nT96UsNHVKo9ABIDrLiyHXZhYkKkIFEQyCsTRiMkCBTqtVrUkISszXTUDgag34SeciIiIiIi6FYeQ5q8B0L/IKu/Hlx6IZQRWsxGjBucqzmdmJGYUiK9Ra2QYJQYoYseYTUC9HwMFRERERETU6eZ/vRvT//o1PvlmT5trnUIjw0yTQfFwHp9RUO+M7efa0qHRKMsFLEJDw2hGQVujEeVzWYnTDXLZn4COAQwUEBERERFRp6qodeOD5bvR1OzDB8t2y00FkxFHI9osaYoJBPE9CsTAgVqpQHzpgSRJqBMmGKQMFDCjgI5RDBQQEREREVGnWrymQt4OSxIqappTrhdHI9osRuRYYz0H4jMKGoSMghybSqBAKD1wt/jh8gYUIxZTlR7kqwQFOPGAjgUMFBARERERUafxtAbw7cYaxbF9te6UrxFHEdrMxoSMgrAkyfttZxQoSw/qhEaG5nQ9zOmGhNdEFWYnlh4wo4COBQwUEBERERFRp6msdSMYCiuO7atJHSjYX+eRt7My0xUBgGAoLPcaANqRURBXelDr8Mr7uW089BfnmjGwxKY4lmdPDB4Q9TYMFBARERERUadxCP0Goipqk5ceBENhbN4XG3c4uI8NGWl6GIWRhM0HMw7CkoQGoWdBblsZBS1+RX8CtdICkUajwfUXDZb3dVoNCnPNKV9D1BswUEBERERERJ1GnGAQdaDei0AwpLIa2HXABZ8/ck6v02JQqR0AYFb0GohkFDR7/IpshXZlFDS1bzRiVN+CTEz72RD0L7Ji8oWDYclIXqpA1Fvou/oGiIiIiIio93J4EjMKwpKEqgYv+hZkJpzbsLtR3h5cakOaQQcAsGQY0NQcCTpEAwX1Qn8CU5oeGWmJjzdWcyyjIBAMY3ulQ95vK6Mg6vQTi3D6iUXtWkvUGzCjgIiIiIiIOo1TpfQAAOqdrarHt1U45O1hZdnytvhNvqc1Eihoqz8BEJl6YBOCBeLPLcphGQGRGgYKiIiIiIio0zg9iaUHQOKYQwCQJAmVwkSE44qt8rY5PZYtEM0oaGviARDpM1BWmJi5AACFOWxMSKSGgQIiIiIiIuo0YkaB2C+gUSVQ0NTsg9cXlPf75FvkbUVGQUtkTXsyCgCgrMiacMySYWC/AaIkGCggIiIiIqJO4xR6FBwnPLCL0wqiKuti2QTZ1jSY02MP8mrNDBWBgiQZBQBUMwqKmE1AlBQDBURERERE1CmCobD8UA8oSwnUMgoqhLKDPnkWxTmLWqBAuEZuqowClUBBYTYDBUTJMFBARERERESdIr6RYX8xo0ClmWFlnUfeThkoaFXpUZAiUGCzpKE0X3k9NjIkSo6BAiIiIiIi6hQOoZGhOV2PAuFbfKfHj0AwrFgvNjLsk698kDcrehQE4G0NoMUXko+lKj0AgJsvHQKTMD5RzG4gIqXEQaNEREREREQpSJIEjUbT5jpHcyyjwGZJQ1ZmGjQApIPHmppbkZ8VCR4EgmFUNXjl9aXxGQXpytIDccyhUa9VNEpU068wEw9cPwqffrsPfQssGFxqb/P+iY5VDBQQEREREVG7SJKEz77bh3kr96CsIBN3ThoOU3ryR4qfdtXL23m2dOh1WtgsRjgOliQ0uHxyoKCqwYOwFAkh6HUaRfYBAJgzYj/H0xJUlC5kW9PbFbgoybPglsuGtuOdEh3bWHpARERERETtMv/rPXh/6U74/CFsrXDgzU83Q5Ik1bWt/iC+21wr748ZUgBAWSIgNjQUJx4U55ih1ykfVcQeBWFJQoWwPlV/AiI6dAwUEBERERFRm8JhCZ9+t1dx7IdtdfhpV4Pq+u+31sHnj/QQyEjTY3R5HgDAajbKa1zC6MTKWqGRYVzjQQAwpxsg5gzsPuCSt/PtGe1/I0TUJgYKiIiIiIioTQ63D/5AOOH4zv0uldXApj2N8vYpx+fDaNABAGxCoMApBArEDIH4iQcAoNVqFGUOu6piPzePgQKiDsVAARERERERtUkcRSiqd7aoHt+x3ylvD+mXJW8rMgq8YkZB8okHURZT7LXN3oC8XZDFQAFRR2KggIiIiIiI2iROGWjruNPtQ50jdnxgiU3eVis98LYGFdkFJbmJGQWR4+oBhHwGCog6FAMFRERERETUJjEgYBZKANQCBWI2QVZmmqLZoNWUGChobI5dQ6/TwG6JrRH1yVMPFLD0gKhjMVBARERERERtEscRlveNlRI4mn0IBJW9C8RAgZhNAKhnFDQ1++RjWZlpSUcdlqo0OczKTJP7HxBRx2CggIiIiIiI2tQg9CIY1MeG6LO8BOWYQwCorItNMDiu2Ko4ZxOyBZpbAgiHJcXrszKTjzpUCxQwm4Co4+nbXtI9LFiwAG+//TY2btwISZJQWlqKSy+9FFOnTkV6uvJfJuFwGO+88w7mzJmDXbt2QZIklJWV4fLLL8eUKVNgMBgSrh8MBjFnzhzMnj0be/bsgV6vx4knnohbb70Vp5566tF6m0RERERE3VK9K/atf0GWCdmZaWg4eKzO2YKCbJN8vqbRK28X5cSOA8rSA0mKBAvEjIJsa1rSe8i1Z8Co18IvZDBwNCJRx+sRGQUvv/wy7r77bvzwww844YQTMHbsWDQ2NuLFF1/EVVddBaczltoUDodx55134sknn8Tu3btx8skn49RTT0VFRQX+/Oc/Y+rUqfD7/YrrS5KE+++/H4888ggqKysxbtw4DB48GCtXrsSUKVMwZ86co/2WiYiIiIi6jbAkKUoPcm3pyLXFHtDrhcaFgWBIsbYgSxkoSDfqYNTHHkNcHj8a40oPktFqNCgtUGYVnH5i4SG8EyJqj26fUbBmzRq88sorsFqtmDlzJo4//ngAgNfrxV133YXly5fjxRdfxKOPPgoA+OSTT/D555+jb9++eOutt1BQUAAAqKurww033IDVq1dj5syZmDZtmvwz5syZg48//hhDhgzBjBkzYLfbAQDffPMNbrvtNjz++OMYN24cioqKju6bJyIiIiLqJN7WAOYs2wW9VouJZx2HNGPyOn+Xx49gKPYtfo4tHbn2dGytiOzXCWUJtY5WSAe3dVqNopEhAGg0GljNRrkJosvjR5NQepCdovQAAM4dWYJd+10wGnWYckm5ol8CEXWMbp9R8MEHHwAAbrnlFjlIAAAmkwl33XUXAGDp0qXy8eXLlwMAJk+eLAcJACAvLw8333wzAOC7775T/IzXXnsNAPDQQw/JQQIAGDt2LKZMmQKfz4eZM2d23JsiIiIiIupCkiThH/M3YckP+7F4TQWW/rg/5foD9bGeA5kmAzLS9Mixxh7oHc2xjF2x7CDXngG9LvGRI76hoZhRkJ0iowAAxp1QhD/fPg4v3nEGThvKbAKiztDtAwWPP/44Pv30U1xzzTUJ50KhEABAp4tFP6Pb1dXVCevr6uoAAFlZsajjjh07sG/fPuTk5GD06NEJr7n44osBAF988cURvAsiIiIiou5j5YZqrN/ZIO+v21Gfcn1lrVve7pMXSf1XPuzHHvTFQEFhlnr/ALFPgTO+9CBFj4KobGt6ygwIIjoy3T5QoNfrMWDAAFitym6pVVVV+NOf/gQAmDhxonz8rLPOAgC8/fbbePvtt9HU1ASn04n//ve/+Mc//oGMjAzceOON8vpt27YBAAYNGqQ6hiV6fN++fWhpaUk4T0RERETU03z9U5Viv1p4uFdTURcLFEQnD9jM4sN+QN6uaYpdS2xwKLILkw8ONHjg84fk/bZKD4io83X7HgXxnnnmGaxbtw7r1q2DRqPBzTffjF//+tfy+fHjx2PLli1444038MQTT+CJJ56Qzw0bNgxPPfWUooShtrYWABRlCqK0tDRYrVY4nU7U19ejtLS0k94ZEREREdHRUetQfgHmcPvh9PgVD/8icdxhSZ4ZQPKMgurG2LWTBQqyhbKF7RUOeVuv08BiSpxQRkRHV48LFMyZMwculwsAYDQaUVdXh/r6euTn58trzj33XHz//ffYvHkzhg8fDkmS8NNPP2Hz5s1444038Mc//lEeqejxRP6lFz9iUZSWlqZY21EMBh3y8jI79Jo9FX8P1B78nFBb+Bmh9uDnhNqjN39OAsGwYhxhlKMliIFlOQnHQ2EJVUKPguGDC5CXl4mgJpac7G4JIDvHAp1WA4cn1q9gYN9s1d9l/z52ebumKRZYKM6zoCDfmrC+O+rNnxHqOD31c9LjAgXz5s1DVlYWtm3bhueeew7z58/H2rVrMX/+fJhMJnzwwQf4wx/+gNGjR2PRokXIyYn8y66hoQHTp0/H/Pnz0draildeeQVArKeBWtkBEREREVFvU+9ogSQlHt9R6cDJQxKzbKvq3fAHIxMPtBqgtDDy4GMXmg6GJcDl9sGemYZGYTRijl39y7i8LPVMg/5Ftna/DyLqPD0uUBAdUTh8+HC8/vrrmDRpErZt24b33nsPEydOxB//+EcYDAY899xzcpAAAHJycvD888/joosuwuLFi7FlyxYcf/zxMJsjqVOtra2qPw8AfL5IxDUjQ70Zy+EKBEJwOFLXg/V20QhbXV1zF98JdWf8nFBb+Bmh9uDnhNrjWPicbN3TmPS42vvesK1W3s6zZ8Al/P3VaNDCH4gEEXbta4Q9M00xRhGBoOo1teFQwjEAyLOldfvf/bHwGaEj1x0/J3a7CQZD+5qAdvtmhqkYjUaMHz8eALBp0yasX78ebrcb5eXlilKEqOzsbIwYMQIAsGHDBgCx3gTRiQjxfD4fXC4XNBoN8vLyOuNtEBEREREdNfUO9QbdLqFkQCSWBhTlmBXnbHFjDh1CSYPRoEVGmvr3klmZaVBL6I02SiSirtXtAwUvvfQS7r77btVxh0AkWAAAwWAQTqcTgHJcYrzouWAwCAAoLy8HAGzfvl11/fbt2yFJEkpLS2EyqadIERERERH1FPVCaYA5PfYg3+xNEigQJiLkx407tJqVYw7F3gdZlrSk5b06rRZZmYljEKOjF4moa3X7QMHXX3+NBQsW4JNPPlE9/9VXXwEATjzxRAwYMAAA8NNPP6lmCDQ3N2PdunUAgKFDhwIA+vXrh7KyMtTW1uLHH39MeM1nn30GADjnnHOO9K0QEREREXU5MVDQvyjWOLDZG1BbrsgoiJ9iYDPHHvZdHj+a3EKgQCUQIMqxKvsXWDIMirGJRNR1un2g4LrrrgMAvPLKK1i/fr18PBAI4Nlnn8WqVauQk5ODSZMm4fjjj8fo0aMRCAQwffp0OcMAAFwuF+677z44HA6MGjUKw4cPl8/deOONAICHH34Y9fX18vFvvvkG//nPf2AwGHDzzTd39lslIiIiIup0YumBGChwtwQQCocT1osZBQVtZBSIpQf2NgIFlgzlGMRBfWxsME7UTXT7ZoaXX3451qxZg9mzZ+Pqq6/GyJEjYbVasXnzZlRXV8Nut+PVV1+F1Rr5l9xzzz2HKVOm4LvvvsM555yDU045BaFQCBs2bIDD4UBZWRleeOEFxc+49tprsWzZMixduhQXX3wxTj31VDQ3N2PNmjWQJAnPPPOM3ESRiIiIiKi7cbcE0Oz1J/QQiCdJEmrFQEGxchShuyWo6DvQ4gvCKfQuKEzIKBB6FHj9aPHFvofMsqQOFBTnmrF2e+xLuolnD0i5noiOnm4fKACAJ554AqeddhpmzZqFjRs3wu/3o7i4GFOmTMG0adPkhoRAZCrC3LlzMWPGDCxcuBDfffcdAKBv3764/vrrMXXqVFgsytonrVaLV155BW+99Rbmzp2LFStWwGKxYNy4cbjtttswZsyYo/p+iYiIiIjaq6nZh4fe+A4tviAmnX0cfja2LOlal8evKDE4rsgKo14rjz9s9vgVD/+1QtmBQa9NyBJQZBS4/dDpYhkBbWUUnDQwFwu+3YewJOHa8wehJDd1kIOIjp4eESgAgEsvvRSXXnppu9ZaLBbccccduOOOO9p9fYPBgKlTp2Lq1KmHe4tEREREREfd6s01aPFFGnXP+WoXTi7PT+glEFVR65a3bWYjrGYjMk0GNLgiJQPxDQ1rmpSNDLVxpQFWkzKjQDzbVkbBgBIb/u+WU+ELhNC3IDPlWiI6urp9jwIiIiIiIkpue6VTsT9n2a6ka8VAQXQUYabiYV/Z0LCyLrY+vuwAAGwWZUZB0yH0KAAizREZJCDqfnpMRgERERERESXaW9Os2N+4uyHp2rYDBcqMgr3VsfX9VB7oxdIDd4syyBA/1YCIeg5mFBARERER9VAur18x7hAAWnwheFuDquvFQEGfg4ECqyk2fUDsXyBJEvZWu+T9ssLEQIHNpD7O0KjXctQhUQ/GQAERERERUQ+1p8qlerzB1ZpwLBAMoaoh1nNAzigQsgLEHgUOt19RitBXJVCQZtQhzahLOJ5nz+CoQ6IejIECIiIiIqIeak9Vs+pxtUDB3ho3wpIEIPKNf7TnQKaQUeASRiHurY5dO9uapmhcKBKnJETl2TPacfdE1F0xUEBERERE1ENVN3pVjzeqBAp2H4hlH/QrzIReF3kUEAMAzUKfAbH3gVp/giirSqAgP4uBAqKejIECIiIiIqIeShxfKGb6NzgTAwW7hDKF/kVWeVt80HcIUwuqGjzydp88S9J7UOtTwIwCop6NgQIiIiIioh5IkiRUN7bI+0PLsuVttdIDMaPguOJYoECcTtDU7EMoHAYA1Dli106VIWBVaVrIjAKino2BAiIiIiKiHqi5JYAWX2y6wdCyLHm70eVTrHW3BFArPPgfJ2QU5NpigYJQWELTwayC2qb2BQrUMgrymVFA1KMxUEBERERE1APVCP0JzOl69BX6CMRnFIiNCTNNBuQIwQGjQacoP2hwtsLTGoBHGLGY6sE/PqMgzahTXJ+Ieh4GCoiIiIiIeqCaRvEbfxNyhRICR7MPwVBY3q+odcvbffMtCaML84QH+zpHqyKbIC0ukBAvPqPgkjF95UaJRNQz8U8wEREREVEPJDYyLMzOgD0zTd6XADR7YxMM9tXGMgpK8xMnGIgZAPXOFkV/gjx7ekJgQdS3IFNupFiUY8Klp/U7pPdBRN2PvqtvgIiIiIiIDt3+uthUgoIsE9IMOhgNWvgDkUyCZq8fWQeDB2JGQWlB4gQDcUpBvbNVkRGQn2VKeR85tnT89qoR2F7pwHmj+sCg53eRRD0dAwVERERERD2MJEnYsd8p75cVRbIEMjMMaAhEmhG6WyIZBYFgCNUNseyD0vzEQIEyo6AVWm0sg6A9jQmHD8jB8AE5h/guiKi7YriPiIiIiKiHqWrwyoEADYABJTYAgEXoFxA9f6Dei1BYAgDodRoUZidmCIiTDxqcLWhwxpoh5tnZmJDoWMOMAiIiIiKibmL9znqs2VKHs0cWY0CxLem67ZUOebskzwxzugFAJKMgKtqjYH99rOygONes2mgw1xbLGmhs9ikyCrKsDBQQHWuYUUBERERE1A24PH787cMNWPFTFf78zlpFX4F42ytjZQeDSu3ydqZJDBT4AUAxwUAtmwAAsixCI0QpMvkgKltokkhExwYGCoiIiIiIuoHNe5vkRoT+YBh//2gDwpKkunZbhUPeHtQnlnlgyYiVHjQfLD0QAwX5Wer9BtKMOmSkqScbZzFQQHTMYaCAiIiIiKgb2LqvSbFf1eDFgXpPwrqmZh/qhR4Cg/vY5W0xo8B9sPSgVhh1mG9PPsFALSCg12lhEcoZiOjYwEABEREREVE3sGWfI+GYGBCIEvsT5FjTkS30ELC0UXqQLKMAALIsxoRj2Zlp0Gg0KquJqDdjoICIiIiIqIs53D5UN3oTjjeoBQoqxP4EyoaHmRnKqQfe1oA8/QBIHSiwq2QUsOyA6NjEQAERERERURfbITQnFKkGCoSMgkFC2QEQ18ywJaAoOzAatLCZE7MGotSCAllWBgqIjkUMFBARERERdbE91c2qx+udLYr9QDCEyrpY34JBJXEZBXE9Cmoaxf4EGSnLCMTJB6mOEVHvx0ABEREREVEX213lkrf75Jnl7QaXMqOgqsErT0LQaTUozFE2JxQbD4bCEvbVxAIQefbkZQcASw+IKIaBAiIiIiKiLiRJkiKj4OTj8+Xt+GaG+4UpCEU5Juh1yr/Om9MNEJMGxOu2FShQLT3ITFdZSUS9HQMFRERERERdqNbRghZfUN4fPThP3m72BuDzh+T9/ULZQUmeJeFaWq0GmUJWwXah90GOLfVDv1qZwcA+NpWVRNTbMVBARERERNSF9lTFvvUvyDahKMcMnTaWFiCWH+yvc8vbJbmxEgVRflasHCEYCsvbudbUgYJMk1GRVXDLhKEpmx8SUe/FQAERERERUQdbu60OD73xHd75fBukgz0FkhH7E/QvyoRWq0G2MG1AESioFzMK1AMFhdkm1eNtZRRotRrcetlQjB1WgLsmDcfYEwpTriei3kvf1TdARERERNSbBIJhvPnpZnhagzhQ78HowXko75uVdL3YR6Cs0AoAsFnSUOeIBAhcHj8AoNUfVPQsUCs9AJDQ4DAqt41AAQCU981Kea9EdGxgRgERERERUQdau70OntZYz4Hvt9YlXRsOS9grTCboX5QJALCaYin/Lm8kUFDviAUJ9DpN0gd/tYyCjDQdTOkGldVERIkYKCAiIiIi6kDL1x1Q7G/c05h0bVWjV25WqNEAffOjgYLYQ32zJwAAaGyOBQqyMtOgFccbCNQCBTnW1BMPiIhEDBQQEREREXUQb2sQm/Y0KY5VNXhR62hRXb9H6E9QnGtGmlEHINJYMKr5YEZBo8snH8tOMbYwPysjIYjQnrIDIqIoBgqIiIiIiDrI3ppmqLUu3F7hUF2/84DQyPBgfwIAsJrF0oPEjILsFBMM9Dotcu3K8zltTDwgIhIxUEBERERE1EH2Co0JRY3C5ALRxt0N8vagPjZ5O1MoPXCpZRQIUxHUjBqUp9gf0MeaZCURUSJOPSAiIiIi6iB7ql2qx5uafQnHapq88mQDABjWP1vetqqWHrQvowAAJp1zHAaUWLG/3oP8rAycOqSgfW+AiAgMFBARERERdRhx1OGAEit27o8EDhpVAgUbd8eaHJbkmhUP/5li6YEnAEmS4noUpM4o0Gm1GF2ej9Hlh/4eiIhYekBERERE1AG8rQHUNsWaForp/2oZBZv3xpoeitkEgHLqQTAURosvpAg2sOcAEXUmBgqIiIiIiDqA2J/AnK7HoFK7vK8WKKhq8MrbA0tsinPmDAPEwQUH6j0IhsLyfls9CoiIjgQDBUREREREHWBPTSxQ0K8wU1Ee4G4JwB8IyfvhsITapligID8rQ3EtrUaDzIxYVoHY+yDNqENGGiuIiajzMFBARERERKQiHJbw064G1AgP9KnsqVIGCmwWoyIroMkdyypodLUiGIoNUizIMiVcT+xTIPY+yM5Mg0a8MBFRB2MokoiIiIhIxbtfbsfnayoBAKMG56GsMBMXnlyKNKNOdb1YetC/0AqdVgu7JU0uO2hy+eSAQI3QyyArM031mlaTEfvhAaAMFLA/ARF1NgYKiIiIiIhUrN1WL2//sK0OP2yrg06rwfjT+iWs9bQGUOuIPfz3K8wEEAkCyIECoU+BmKVQEFd2ECWWLhyo98SOsz8BEXUylh4QEREREcXxB0JocLUmHN91wKWyOrGRYa4t8q1/lvCw39gcu15NYyyokK9SdgAAxblm1ePZmcwoIKLOxUABEREREVEcsTRA5HAnTi8AlKUBZYWZcg+BLEssUOBo9gvXFzIKstUzCpIFCrKYUUBEnYyBAiIiIiLq9eqdLdhX06yYPJBKdaN6A8Om9gQKiqzyts0Sa0jo8IilB7FAhFojQwAoSRIoYI8CIups7FFARERERL3axt2NeOG9dQhLEjLSdLj9yhMxrH92ytdUNcR6AhTnmuUeAU63H2FJgjZu6sCeqlhJQr+CTHnbLmQUON2RjIKwJKHBKZQe2JP0KLClI82ggy8uuJHNQAERdTJmFBARERFRr/bdphqEpcgowhZfCPO+3t3ma8SMgiF9s+TtUFhCszegWOtuCaDeGes/UFYUCxQoMgoOZiM43X7FaMQcm/qDv1ajQXFuYraB2PeAiKgzMFBARERERL1abZOyjGDnfhe8rcGUr6luiL2mrChTMb7Q0awsPxAbGVoyDIrSALtZyCjw+CFJEhqEoII5XY+MtORJvvF9CrQaDdIM6uMZiYg6CgMFRERERNSrxTcmDEsSNu9tTLpekiRFRkFhtknRlLApLlCwpzpWdiA2MgSUGQWBYBgtviDqhbKDXJt62UHU6PJ8xX5JnnrfAiKijsRAARERERH1Wq3+IJwef8Lxn3YlDxTUNbWg1R/rC1Cca1ak+8c3NBQbGfYrzFScs2QYoNPGAgcOt18xdjE3SdlB1EkDc/Gzsf3k/eOFMggios7CZoZERERE1GvVJhlzmCqjQMwQyLGmISNND7vYayBF6UFZoVVxTqPRwGYxotEV7U/gU/QzSNafQDTxrOPQv8iKOkcLzhxe3OZ6IqIjxUABEREREfVaYqAg3aiTMwXqHK1wefywmo0Jr9krTDAoybMAAOxJMgoSGhnGZRQAgM2cJgcKHB6/Yn1bGQVAJNgwanBem+uIiDoKSw+IiIiIqNeqERoZDumXpegZsPOAU/U1e6tiGQJ9DgYKxB4FYkaBmH2QaTIg25o4kcAmBCOc7vhAQeoeBUREXYGBAiIiIiLqtcRGhgVZJgwotsn7uw641F6CvdViRkGkeaDdop5RsKdK2Z9AbGQYZY8bkdhwiBkFRERHGwMFRERERNSj7DzgxCff7EGj0BQwmd1CMKAgOwMDSmI9BHbuT8wo8LQEUFmrklGQqZ5RUFnnlrf7FSSWHQCATQgy7K5yIRgKy/vt6VFARHS0sUcBEREREfUYTo8fz876Eb5ACKs21+LRqadAq/ItPgDUOlqwv94j7w/plwWHOzYBYXd1MyRJUmQB/PW/6xAMSQCANIMOhdkmAMpAgac1CH8gBKNBp8hYKM5RH12YLbx2e2UsOGEzG5GRxr+OE1H3w4wCIiIiIuoxNuxqgC8QaUhYUevGxt3Jpxes214vbxfnmpGfZUJhjkk+5vOHFGMQK2rdWP7jfnn/0tP6wqCP/HXZajZCDEc43D5IkqRolpifpd5vINeufjzZeiKirsZAARERERH1GOI38gDw+ZrKpGt/3BELFIwYmAMAMKcrv8H3tAbkbXHMYVGOCT8bWybv63VaZJrFXgN+uFsCaPEF5WPJHvzzkpQXMFBARN0VAwVERERE1GNsrXAo9n/a1aB42I8KhcOKHgQjBuQCAHRarSLd39MSe9CvaoyVKZQVWqHVKksaxMkHTc0+RTZBRpoelgyD6j1nWdOg0yaWR+RnmVRWExF1PQYKiIiIiKhHcLp9qGn0Jhyvqk88dqDeC38w0jRQp9WgrDDWaFDMKnALQYbqhth1inMTH+LF6QXxgYL8rAzViQeRn69VHZtYwIwCIuqmGCggIiIioh5hZ5JxhgcaPAnHdlcpRxwaDTp5X/zm39MSCxQcEAIFhdmJjQkVkw/cPtQ0xdbnJ+lDEJVrSzzP0gMi6q4YKCAiIiKiHkF8MBdVtREo6F9kVZwzC4EC98FAQTAURp2QIVCUo5JREBcoqHW03cgwKs+e2Kcg387SAyLqnjiPhYiIiIh6hHpHq+rxqobEAEKqQIFaRkFtUwvCUmQsolarUX3wt8f1KAiFJXm/rUBBfEZBUY4JpnT+VZyIuidmFBARERFRj1AnfIM/pF+WvH2gXplREAiGsb9ObEyYqTiv6FFwsJmhGGwoyjFDr0v8a7JYehDfo6CgjcaEeXGlCZMvGJxyPRFRV2KggIiIiIh6BDFQMGJAjrzd4GyFPxCS96saPPK3/XqdBsW5yn4DFpXSA7GsoSTPovrzc4Uxh/XOVvm1QNsZBScely03Q5wwrh+G9c9OuZ6IqCsx34mIiIiIur1wWEK9M1Z6cHy/LOh1WgRDYUgAqhu96FsQyRyorHPL64pVsgPM6ULpwcGpB2IQoig3sZEhEMkK0Gk1ipIDADAatLCZjaqviTKlG/B/t5yG5pZAm40PiYi6GjMKiIiIiKjbc7iVPQEKskzIEb7hb2z2yduVtbGyA7XsgGQ9CqIKVRoZAoBep0VhduK5fLsp6WhEUUaankECIuoRGCggIiIiom5P/MbfajYizaiDXfgW3+mOBQoqhIyC0vzEQIE5Q+hR0BpMuH5hjnpGAaCebcAxh0TU2zBQQERERETdnjiKMO9gJoHNIgYK/PK2WHrQJz/xwd4cl1EQDIXR4IqVNSQrPQCAYpVsAwYKiKi3YaCAiIiIiLo98Rv/3IPp++K4QocnEiho9voVQYM+bZUetAZQ72zFwcmI0GhSP/jHN0YEGCggot6HgQIiIiIi6vZqGsVRhJEHc2VGQaT0oEboNWBK06s2GRSbGUoSsLe6Wd7PtWfAoNclvQ/VQAH7DhBRL8NAARERERF1CZ8/hB+21cHp8be5tqYxNr6w4GBDQbtZyCg4mEXQKJQQ5NjSVZsMmtL1EA/vrnLJ20Up+hMAQGG2SRGgyMpMw8ASW5v3T0TUk3A8IhEREREddaFwGM/OXoud+12wW4x45raxMBrUv8mXJEmRKRCdPKDIKPBEMgoaXbGmhjnW2FQEkVajgSXDgGZvZOLBzv1O+VyBylQDkV6nxf2TR+GHbXWwmowYNTg36X0TEfVUhx0ouPHGG9v/Q/R6pKenIzc3F8OGDcMll1wCm42RVyIiIqJj1aLVFdi5P/JNvsPtx64DLhzfL0t1rcPthy8QkvdjpQexjAKn2w9JkhQZBVnW2Pl4VrMxFig4IGQUpGhkGFWYbcKlp/Vrcx0RUU912IGCVatWAYCcziVJUsIatXMajQYvv/wyXnjhBZxyyimH++OJiIiIqIfyB0KY9/UexbHKOnfSQIFYdpBpMsB0sMeAXcgoCIUleFqDaGyOZRRkZ6YIFJiM2A9PwvFUoxGJiI4Vhx0o+M9//oOZM2di8eLFyMvLw8SJE3HCCSfAYrHA4/Fgy5Yt+PDDD1FZWYkTTzwRF154IRwOB5YvX47t27fjjjvuwEcffYTCwsKOfD9ERERE1M3VOVvh84cUxyrrEh/ao2qaEvsTAJFmhXqdFsFQGADgcPuUPQqSlB4AkYwCNYUq4w+JiI41h93MMBAIYPHixTjllFOwYMEC/O53v8OFF16IsWPH4oILLsAdd9yBTz/9FOeeey42bdqEUaNG4b777sP8+fPxy1/+Ek6nE2+//XZHvhciIiIi6gEcbl/Csf117qTr1SYeAJFMVbti8oFfESjIThUoMKkHCtpqZkhEdCw47EDB66+/Dr1ej+eeew4WS+J8WgAwGo146qmnoNPp8Oqrr8rH77vvPqSnp2Pp0qWH++OJiIiIqIdyNCcGCirrPQirlLICwK4UUwnEhob1zha4DvYdAIDslD0KDAnHzOl6WJIEEIiIjiWHHSjYsGEDBg8ejPz8/JTrsrKyMGjQIKxbt04+ZrFYUFpaiqqqqsP98URERETUQ6llFPj8ITQ4W1WPi1MJykvtivM2YUTi7qpmeVsDwG5J3aMgXq49Q2UlEdGx57ADBVqtFh5P8loykdvtRjgcVhzT6zmZkYiIiOhY5HD7VY/vV+lTsH2/A6FwJNMg3ahDWVGm4ryYUbBbyDywWYzQ65L/VVetR0E+AwVERACOIFBQVlaGffv2Yc2aNSnXrV27Fnv37kVZWZl8zOfzYd++fSgqKjrcH09ERERE3YgkSUlLB+KpZRQAyqaFUZv3NMnb5aV26LTKv77ahQf+itpYn4NcW+qHftVAQRYDBUREwBEECn7xi19AkiTceeedWLZsmeqalStX4q677oJGo8Hll18OINIE8fnnn4fX68Xo0aMP98cTERERUTexeU8j7vrLcjwxYzXqHC1trhcDBaa0WJapOAYxamuFQ94eUpadcN6WpLygODd1U0K10oOSPDYyJCICjmA84sSJE/HFF19g6dKluO2221BUVITy8nKYTCZ4PB5s27YNVVVVkCQJY8aMwQ033AAAuO222/DNN99Ap9Phpptu6qj3QURERERdZPaXO+BpDcLT6sYL763DQzeOhik9sVlglKM5VnpQ3teOtdvrAQDVcYGCYCiMfTWxvgODS20J1xKnHojaDBSoNDMcflxuytcQER0rDjujQKfT4aWXXsKtt94Kg8GAAwcOYMmSJfjkk0+wdOlSHDhwAHq9HlOmTMHrr78OnU4HANi1axeysrLw3HPPKcoRiIiIiKjnqWnyYp+Q8l/d6MXSHw8kXS9JkiKjoLxvlnAtZTZCRa0bwVCknEGv06JPXuKkLbGZoag415Tyvg16nfI6FiNM6eyhRUQEHEFGARAZf3jPPffglltuwVdffYXt27ejqakJGRkZGDx4MM455xzk5OQoXjNjxgyUlpbKgQMiIiIi6rnWbKlNOCZmAcRztwTk5oQAcHxfu7zd1OyDLxBCmiHy90SxOWHfAotqc8KkGQU5bZcRFGab5CyGi0/p2+Z6IqJjxWEHCioqKlBaWgoAyMzMxIQJE9r1OmYREBEREfUOkiTh2001Ccdrm5L3KRAnHhgNkSwBvU4jZw7UNrWgND+SObD7QCxQ0L/Iqnq9TJMRGg0g9lHMSNMhKzP5aMSoK87oj5kLt2JAiQ0XnNynzfVERMeKww4UXHTRRTj55JMxceJEXHLJJcjIYJdYIiIiomPJht2NqiMNUzU03LI3NsUgOzMdWq0GefYMVDVEvtmvafTKgYJdQkbBcUkCBVqtBlazEU4hAFGcY4ZGo2nz/k8dWoAxQ/LbtZaI6Fhy2D0KAGD16tV48MEHcfrpp+PBBx/E6tWrO+q+iIiIiKibW/DtXnm7X2GmvB1pbBhIWC9JEpavj/UvGDk40jywICvWT6D2YJBBkiRFZkJpQWJ/gqjMDGX5QbLsAzUMEhARJTrsQMGSJUtwzz33YMCAAfB6vZg7dy5uvPFGXHjhhfjb3/6GAweSN7EhIiIiop7N3RLAln0Oef/a8wfJvQUA9fKDPdXNqBQyEM44sQgAkGWNlQlEMwPiexmkKiVweXyKfZYREBEdmcMOFBQWFuLWW2/Fxx9/jLlz5+KGG25AdnY2Kioq8PLLL+OCCy7AzTffjPnz58Pn87V9QSIiIiLqcv5ACN7WYJvr9lbHGhZaMgwY1MeGPHu6fEyt/GDTnkZ5e2CJDUUHGw7aTLGMAOfBh36xlECv08KUlrxidszQAnl7+IAc5GelnnhARESpHVHpQdTQoUPxhz/8AcuWLcNrr72G8ePHIy0tDStXrsR9992H008/HY888khH/CgiIiIi6iSVtW787pUVuPvl5diwuyHl2j3Vsf4B/QozodFEeg1EqQUKxOBCuTDtwCpMLnB5IgECh5AlYLcYU5YIXHByKQb2sWFYWRZu/tmQlPdNRERt65BAQZROp8PZZ5+N559/HitXrsQDDzwAk8kEt9uN999/vyN/FBERERF1sHe/3I4WXwjBkISPlu9OuVZ86C872J8gPysWKFArPdgrjE3sVxDraWAzixkFkUCBmFFgSzICMSrfnoEHrx+N6deMhNWUei0REbXtsKceJFNfX49PPvkECxcuxI8//ohwOAwAKC8v7+gfRUREREQdZF9NMzbtiU0k2HnAhepGLwqz1dP491QnPvTn2mKBgkZXq2K9pzWAOkfsWJnQ/NBqVskocAsZBea2Rx0SEVHH6ZBAgdfrxaJFizB//nx8++23CIfDkCQJNpsNl112GSZOnIihQ4d2xI8iIiIiok6wbF1iI+qVG6ow8awBCcc9rQHUOxMf+sXMgGavcurBPiGwYE7XI8cW62cgvs7TGkQwFD6kjAIiIupYhx0oCIVCWL58OebPn48vv/wSra2tkCQJWq0WZ5xxBiZOnIjzzz8fRiP/xU5ERETU3VU1eBOObdzdhIlnJa7dL0wuMKXFHvozTQb5uMvrV7xmj1h2cLCnQZQYKAAiWQUOjxgoYEYBEdHRdNiBgjPPPBNNTU2QpMjYmn79+mHSpEm44oorUFBQ0MariYiIiKg7qXcm9hRoUDkGAAcaYoGC4lyz/NBvjcsokCRJPrf7gLL5ocig1yEjTY8WX2TagtPjh1NResAvnoiIjqbDDhQ0NjbCZDJh/PjxmDhxIkaPHp10bXV1Nd577z3cddddh/vjiIiIiKiThMMSGl2J46xd3gD8gRCMBp3ieLWQfVCYE+thkCk0EgyFJXh9QZjTI1kGO4VAwYBiW8LPspqNcYECZhQQEXWVww4UPP3007jkkkuQkZGRdM1XX32Fd999F8uWLUM4HGaggIiIiKgbamr2IRSWVM81uFpRlGNWHFNkFAjnTOl66LQa+Voujx/mdAMaXa1oao4FIgYUWxN+js1sRE2jV35d/HhEIiI6eg47UPDzn/9c9XhjYyP++9//4r333sP+/fsBQJF2RkRERETdi1h2YDUbYdBp0XBwaoFaoCBZRoFWo4ElwyCPOGz2BlCUo8wmyLWlq2YIiGULNY1e+ANheZ8ZBURER1eHjUdctWoVZs2ahc8//xzBYFDuXZCRkYHLLrsMkydP7qgfRUREREQdSBxbmGdLh04MFDiVYw59gZDiWHGOcnxipskoBAoi/79zv1M+P6AksewAUDY0FEcv6rQaRZNEIiLqfEcUKGhubsbcuXMxe/Zs7N69GwDkAMGgQYNwzTXX4IorroDFYjnyOyUiIiKiTiFmFOTY0qHTxjJBowGDqJpGL6JFCnqdFrk2ZRmq1WwA6iLbroMjEitq3fL5/kWJZQdA8kBBVmYatMxMJSI6qg4rULB+/XrMmjULCxYsgM/nk4MDJpMJXq8XBQUFmD9/fofeKBERERF1jnohQyDPngHxubzBqWxyWN0YKzsoyM6AVqt8iLcKDQ2bD2YW1DbFAhFFcRkI8uuEQEG0qSEQCRQQEdHR1e5Agdfrxfz58/Huu+9iy5YtACLZA3q9HmPHjsXll1+OCy64ACNHjmQ/AiIiIqIutnlvE3YdcOKME4varPGvcygzCsS/ycVnFIhr8+2JTa0tQpmAy+tHIBhGo3CN/Cz1Rti2JCMQGSggIjr62hUoePzxxzFv3jx4vV45e2D48OGYMGECJkyYgOzs7E69SSIiIiJqv8paN56f/SNCYQlb9zlwz9UnJV0bliRU1sVKA4qyTQiEYo0E43sUiNkBag/9YkaByxtAvbNFLlXQajTIsaar3oc1SaAgO8l6IiLqPO0KFMyaNQsajQYjRozAeeedh/Hjx6O0tLSz742IiIiIDsN7S3bIIwo37G6ELxBCmkGnurbe0YIWX0je71uQqcgAcHr8iglWYkZBnkpGgfjA7/b6FYGFHFsa9Dqt6n0wo4CIqPtQ/zd1Env27MHatWuxcuVK1NXVddY9EREREdFhcrp92LinUXFsr9AcMN7emlg2QUFWBjLS9LBkxMoHgqGwYlRhW6UH4oQCp8cfl4Gg3p8ASJFRkMmMAiKio61dgYJnn30WY8eOhcvlwpIlS/DYY4/hnHPOwdSpU/Hhhx/C4/F09n1iwYIFuP766zFy5EicdNJJuOyyy/Dqq6+itbVVdf22bdswffp0nHHGGTjhhBNwxhln4L777sO+fftU1weDQcyePRsTJ07EqFGjMGbMGEybNg3fffddZ74tIiIiog714456SJLy2M4DTvXFUAYR+hVmAgDMGcpxhO6WyPSCYCiMRlesuaFaRoHNHMsAcLr9qG0jsBCl12lhTk9Mds22MqOAiOhoa1egYMKECXjzzTfxxRdf4I477kBJSQlCoRC++eYbPPDAAzj99NMxffp0LF26tFNu8uWXX8bdd9+NH374ASeccALGjh2LxsZGvPjii7jqqqvgdCr/47do0SJMnDgRH3/8MfLz83H22WfDaDTio48+wi9+8YuEYIEkSbj//vvxyCOPoLKyEuPGjcPgwYOxcuVKTJkyBXPmzOmU90VERETU0aoavAnHdu53JV2/t0YIFBREAgV6nRbpxlipQjRQUO9slfsNaDSRxofx7JZYZoDXF8R+of9BskaGUWpZBSw9ICI6+g6p9KCoqAh33HEHPv/8c8yYMQMTJkxAWloaWltb8emnn+J//ud/AAAtLS3YtGlTh9zgmjVr8Morr8BqtWLu3LmYOXMmXn31VSxevBhnnnkmtm/fjhdffFFev3//ftx///0AgOeeew5z587FX//6VyxcuBBXX301HA4HHn30UcXPmDNnDj7++GMMGTIEixYtwiuvvIK33noLb775JoxGIx5//HFUVVV1yPshIiIi6kzi+MKonfvVMwokSVJkFPQ9mFEAQFF+4GmNBArEsoPszHTVfgNWs1ExNWFbRexntxUoiO9ToNNqkpYkEBFR5zmkQIHotNNOw7PPPosVK1bg0UcfxYknnihPRHC5XJg0aRKuuOIKzJw5Ew6H47Bv8IMPPgAA3HLLLTj++OPl4yaTCXfddRcAKDIZ3njjDXi9XkybNg0TJkyQjxsMBtx///0oKipCfX09fL5Y2txrr70GAHjooYdgt9vl42PHjsWUKVPg8/kwc+bMw34PREREREdLtUpGgdPjlx/2RU3NPjlbAIhlFADK8oPomrYmHgCRbASxT0FYqINIVXoAIGGMY35WBrQcu01EdNQddqAgymKx4Nprr8V7772HTz75BDfddBOys7MhSRK2bt2Kp556CmeddRbuvvvuw7r+448/jk8//RTXXHNNwrlQKNKhV6eLpcZ99tln0Ol0mDp1asJ6s9mMpUuXYv78+UhLi/yHaMeOHdi3bx9ycnIwevTohNdcfPHFAIAvvvjisO6fiIiI6GgJhsKoc7aonhOzAaLEbIIca7oii8Ai9AvwHAwU1DTFghBq/Qmi7Bb1coFUrwGUoxUBYHR5Xsr1RETUOdo1HrG9BgwYgN///ve49957sXTpUsyZMwfLly+H3+/HwoULD+8G9XoMGDAg4XhVVRX+9Kc/AQAmTpwIAKisrERjYyMGDhwIu92OvXv3YsGCBaioqIDNZsN5552Hk08+WXGdbdu2AQAGDRokj/0RRY/v27cPLS0tyMhI/R84IiIioo5U2+TF91vrMHxADkryLG2sbZEbGWo1GuRlZaDmYClCvaMVZYVWxXpFfwKh7ABQzyioaYwFGwqzk08wsGemYV+tW3EsKzMNxiQjGqOCobBi/9ShhSnXExFR5+jQQIF8Ub0eF1xwAS644ALU19fjgw8+wIcfftgh137mmWewbt06rFu3DhqNBjfffDN+/etfAwD27t0LAMjPz8ebb76J559/HoFALJ3un//8Jy6//HL83//9H4zGSMS6trYWAFBQUKD689LS0mC1WuF0OlFfX4/S0tIOeR9EREREbQlLEl54fz1qGr1YtLoCz9w2FmnG5A/bYn+CPHs6irJNcqCgrYyCfgXKIIRFESgIHrx+bNJVykCBJbGvQFtlBwBQ3teOJWv3AwCMei1Kcs1tvoaIiDpepwQKRLm5ubjllltwyy23dMj15syZA5cr0rnXaDSirq4O9fX1yM/PR3Nz5D92GzZswDfffIPrrrsON954I7KysvDNN9/giSeewLx582Cz2fDQQw8BgDzaMT09+YzeaJlCR4+BNBh0yMvLbHvhMYC/B2oPfk6oLfyMUHv0pM9JRU2z/KDv9PhR2dSC004oSrre/VO1vF1aaEVxrhk/7qiPnPOFEt57RV3s7zbDywsU5/NzYoGDEAB7lgkNzthY6qGD8pCXJMOhKD8TgLIRdN8ia5u/+4uyTPh+ez2qGzy465cju/SfVU/6nFDX4GeE2qOnfk46PVDQ0ebNm4esrCxs27YNzz33HObPn4+1a9di/vz5coNCl8uFq6++Gg8//LD8uosvvhgFBQW45pprMGvWLNxyyy0oKCiQ+xuolR0QERERdaWdlQ7F/rrtdSkDBfuEUoI++RZFT4DqBuUXHk2uVjS6Yg/+A0psivOZ5lhGgcvjR3WDF+GDZQ06rQYFKTIKcqyJX8AUtSM7wKDX4ZFpp7W5joiIOlePCxQUFUX+4zh8+HC8/vrrmDRpErZt24b33nsPxcXF8ropU6YkvPakk07CsGHDsGHDBqxevRoTJkyA2Rz5j1Zra2vC+qhoAKKj+xMEAiE4HImdiY8l0QhbXV1zGyvpWMbPCbWFnxFqj67+nOyvc2P2lztQnGvGVecMUB0tGO+n7XWK/dWbajDxjOT3LwYWss1GZOhjP+NAnVvx3tfvbJC3bRYjgr4A6uqEyQjBWL+AJmcLNu2I3UuuPQNNjckzLXWQEo6Zjboe8We0qz8n1P3xM0Lt0R0/J3a7CYY2esVEHfHUg65kNBoxfvx4AMCmTZuQk5Mjn+vTp4/qa6LHm5qaAMR6E9TV1amu9/l8cLlc0Gg0yMtj510iIiI6PK9/vAkbdjdi0eoKfLBsV7tes6fKpdivafSiXqXXAACEwxKq6mMP7yV5ZuTaY9/s1ztbEQ7HHuAVjQwLElNj45sZ1gj9DwqTjEaMUpt60J4eBURE1D10+0DBSy+9hLvvvhvV1dWq56NNCYPBIAYPHiyXENTU1Kiur6+P1OlFgwrl5eUAgO3bt6uu3759OyRJQmlpKUym5Cl2RERERMnsq2nGvprYFIAF3+3DnmpXildEHvz3xk0OAIADDerZiHXOFvgPZgFoABTnmJFniz2ch8ISHG5f7J6qUwcKLHGBArFRYqqyAyDS6NBoiP01M9+egZI8NiYkIuopun2g4Ouvv8aCBQvwySefqJ7/6quvAAAnnngiMjMz5fGH8+bNS1hbV1eHTZs2QafTYfTo0QCAfv36oaysDLW1tfjxxx8TXvPZZ58BAM4555wOeDdERETU09U7WvDwP7/DH17/FrVN7SshXLkh8QuPdTsaVFbGVDV64fOHEo43e/2q6/cLjQnz7BlIM+qQZtQpHvgbm2OBgj3VyUcjAoAlI1ah6m0NKqYm5LeRUZCRpsf0q0/C+NP64sZLyvHITae0q9SCiIi6h27/b+zrrrsOAPDKK69g/fr18vFAIIBnn30Wq1atQk5ODiZNmgQA8nSFf/zjH3IQAQDcbjcefPBBeL1eXHrppYpxiDfeeCMA4OGHH5YzDgDgm2++wX/+8x8YDAbcfPPNnfcmiYiIqMf4z6Kt2F/nQVWDF4tXV7brNd9vrU045vKoP/BH7YhrZBjV7A2oHq+si2UfiN/e24RRhc6DGQXulgAahEaGbWUUSAB2CWUQubbk06KiBvWx4xfnDMQ5J5XAlN7j2mIRER3Tuv2/tS+//HKsWbMGs2fPxtVXX42RI0fCarVi8+bNqK6uht1ux6uvvgqr1QoAOPvss3HnnXfi5Zdfxq233orhw4cjJycH69atQ2NjIwYPHiyPRoy69tprsWzZMixduhQXX3wxTj31VDQ3N2PNmjWQJAnPPPOM3ESRiIiIjl3ulgA27GqU97/4oRLXXTQ45WtafEE0uHwJx11JMgOitlc6VY8ne11FrRgoiI0ttJuNcraB82BwYr8QVLBkGJBtTewpkJGmh9VkgOtgYMIfiDU3zLWx3wARUW/W7QMFAPDEE0/gtNNOw6xZs7Bx40b4/X4UFxdjypQpmDZtmiI7AADuuOMOjB49GjNmzMCPP/6IrVu3oqSkBJMnT8a0adMSeg1otVq88soreOuttzB37lysWLECFosF48aNw2233YYxY8YczbdLRERE3dQ3cSUEep0GYUmCNsWYZbG2X5QsMyBqu5BRkGdPR52j9eDr/j979x0gR133D/w9W29v65W9frnUSw89IQGVoqKgiCBqBMHAo1geFZ8fKCqK+qCCgqIgiOgjRUFKkKaggEBICCWQRuqlXe93e7t7t7d1fn/s7cx3dmd2LyHlLnm//pqdndmdI0Ny3898Sm6gIJWSsb15UHk9VSgl8AqNBQPh9LmZzwKAyhKH7phoSZIwpcqtCYxklI0jo4CIiCavSREoAIBzzz0X55577riPX7p0KZYuXTru461WK1asWIEVK1YcyOURERHRMeDdvdpFcyIpo3cwkre5X5dB80GjXgMAMBiKahbzJzVW4Lk3W8bOyw0wNHeHMDyaAACYJAlzppQo73mduaUHfUNqv4HyPNMIGipzAwUepw32cY7XIiKiyWnC9yggIiIimih6dUYTtupMJhB1ChkFfmFcYb4eBXs61H4ApR67ptmg3nlbhADG9FqPpieAmFGQKT3oG1KDEPn6Dej1LhhPfwIiIprcGCggIiIiGgdZljUNADNaCgQKxNKDWXU+ZXt4NIFkKqVzBtDRr04waKh0w1OsNhbUy0TYuk8NFMyfWqp5z6dpZjgWKBACHnkDBTrTEBgoICI6+jFQQERERDQOQ8MxxBO5C/u2QoECofSgsd6neS9s0KegUwgUVJc54RbKB4IjcciyrL0GYTTi7KzvEEsPAsPp0oNeMaMgT+lBubcIzqyJBWxkSER09GOggIiIiGgcxHR90WA4d6JBRkqW0T2oBgoaKt2wWdRfv4JGgYI+9ZzqsmJ4itXFfjyRwmgsqbweGY0jHFE/p6pM2y9BLD0IDccRTyQRCKnXnC9DQJIknLu0QbNPLJ8gIqKjEwMFREREROPQp9OfAFAbBOrp6h9RshAkAJWlDriFRb/uBANZRueANqPA5bBCnEsQEgIDPcJ12awmTQYBoM0oSMkymrvCyOQjSADKPPkX/h9ZPAUXnD4NFrOEMk8RTplTkfd4IiKa/CbN1AMiIiKiI0nMKKj1O9E+lu4fHI4bjkjc0RoQznGhyGaBx2lVeh0EdQIFg8EoYnG1xKG6rBgmkwSnw6pkDoSGY6gYKxnoGVQDBRW+4pxRh0U2M2xWk/KZu9qHlPdKPHZYzPmfG0mShPNPn4YPnlyHIpsFJpPxKEgiIjo6MKOAiIiIjkmyLOPxVbvx60c2oqU7VPB4caTgjBqvsp2SZcNeAztaBpXtOVN8AKDNKBjOPU/sT1DitsNhTz/X8Wj6FKgBhm4hUFBZkts/QJIk+Jxq+YEYKCgvkE0gKi6yMkhARHSMYKCAiIiIjknbmgfxzGvN2LynHw+90FTweDGjoL7Cpek1ENApP5BlWZNRMFsJFKgTDPQyCt4VRh1WC/0GxMkH4ojEHqEHQoVOoADQTj7Y1aZeU75GhkREdOxioICIiIiOSS++3aZs72gNGI4qzOgLCJMCvEXwimMHh3MX/D2DEWUcIaBOPPBoehRoMwo6+4c11zVPGHXoE5oSDgrNCDWlB0aBArd6rthAkaMOiYhIDwMFREREdExKJLUjBrsG9JsVAkAqJSt9BYBMoEBdfIsBgQwxxb+6rFgpOcjXzPCl9e1IpmTlOz54Up3yXolH/b6BoFGgQDvxQDlXCBSIOOqQiIj0MFBAREREx6TugRHN69Y8fQoC4aiygAfSC2xxmsDQcG7pwZ7OoLIt9jTIV3rQ2a9e09kn1cFmNSuvS93q0//BUDpoEY0nNdkMFQalBCVu/cwBjjokIiI9DBQQERHRMScSTWjGCgJAS3fY8HixP4G72Aq7zaxpEBjQySjY06EGCqbXeJRtsSlhdumBOIKxMis7oFTIChgYKz3oF67LbJIMMweM9pex9ICIiHQwUEBERETHnLbe3KBAS49xRkGvsIDPpOvn61EQiyfR1qN+hyZQYFB6kErJmoBE9tP+UmFCwUAwClnWHl/ithtOJShx5QYKzCZJk6VARESUwUABERERHXP2deUGBVp7jDMK+nUW8JpAQdbUg5busFKqYLOaUOt3Ku+JpQeRaBLxRLqJYk55Q1YZgdijIBpPIhJN5PRNMKKXUVDqMQ4sEBHRsY2BAiIiIjrmbNrVl7MvNBJHJJrQPb53SM0oyKTre53GzQybhX4HDZVumE3qr1xioCD9velzxawFj9MGu9CfAADcDissZvVzBoJR9Olclx6vy4bskAAbGRIRkREGCoiIiGjSW7ulC796ZAPW7+wteOzwaBzbWwK674lP6DX7xYyCsQW2x6ku+EMRba+Bjr5hZbuuwqV5z2oxw2FXgwCZPgVizwS9JoOSJGX1KRjVXFe+hb/FbMoJUBiNUiQiImKggIiIiCa117d04Z6nt+LdPQP4/VNbckYOZtu0q19J8XcXWzWLcrHmX9QbyE3xdzvU0oNINIFEMqW8FgMFNWVq2UGGeG5m8kFfIDcYka00a0SiNlCQv99AMKtx4rIFVXmPJyKiYxcDBURERDRphSNx3PvcduV1PJHCG1u7856zQSg7OGFWuWakYL9OoCAWT2IgJCzIx453ZT2hDwtZBR39aqCgtlwnUCBmI2RKD4Qyguz+BBnimMOBUFQT2Cjz5A8UnNjoV7YrS4sxq86X93giIjp2MVBAREREk9aejiBi8ZRm3+rNnYbHp1IytjUPKq8XzShHmfD0Xqz5z2jpDkMe6zFos5qUDAS71QybRf1VKjz2xD44EtOMPazRCRSIkw+Cw+ljxR4FfoPsADGjoGdwRDNtoVBGwZkn1MJuNcPnsuHqixflPZaIiI5tliN9AUREREQHajCUmwHQ0h1Gz+AIKkqKc97b0zGkPPk3SRLmTCnRlAnoZRTs7Qwq29mNCV3FVgwE0xMPMn0KOoXPczmsOb0BAG1Dw0xGQfeAGiioLM29dgCaHgVNbUPKtiQBPp3JBqL500px2zdOh9VigknitAMiIjLGjAIiIiKatAZDUd39nf0juvs3CM0Op9W4UVxk0TyJ1+tRsLdLDRRMq/Zo3nM51AV/JgDRLvYnKHdC0lmUu4u1PQrCkbimdMEoUFAilBeIP3u5t0gzEcGI3WpmkICIiApioICIiIgmrczT/GziBAHR5t1qf4J5DaUAtNMCdAMFHWqgYGq1W/OeWwwUjGUGdAlBipoy/QW/WHoQGomje1A9x2E3w6OThQBoMwpElTrZE0RERAeKgQIiIiKatPRKDwCgd1A/UNDaHVK2Z9R6AQBlQkZBOBLHaCyhed0tfFZ2RoE7a8EPQHO8UWaA2MwwOBxD94AaKKgsKdbNQgCAUoOGhRx1SEREBxMDBURERDRpDQjp9411XmW7VyejIBpPagIIVWNP+70uG8wmdWEuZims296jbHuKrZoJCYC29CDTo6BHyA4wWsB7s0oPuoT+BFUGwQUAcBZZNA0UM5hRQEREBxMDBURERDQpybKsWdQ3TilRtvVKD8QmgxazhPKxp/MmSYLPpS7cA2H1M197t0vZXjyvMudJvzgiMRyJI5lKacoX9BoqAoDHpZYQDIVj6BIzCvIECiRJ0vQpUM9hRgERER08DBQQERHRhDEQHMXKV3ZjfVNvwWMj0QSi8aTyevYUn7LdGxhFKjPTcEx7T1jZrigphknIIhAnBmSaBPYEItjVrk4WOG1Bdc41ZPco6A9GkUylv1cCUOHTLxXwOtXARDIlY2+H+j2FFv16fQqMAhJEREQHgoECIiIimjDueXor/rG2GXc8vhnNXaG8x4plBxazCdOF/gGJZAqBrIkI7b1qoKAyqySgRHjCn8ko2N0uLt6LMaXSlXMNrqweBWLZQanHDqvFrHvtziKLptyhX8iMyFd6AOQGCkySpJncQERE9F4xUEBEREQTQvfACHa0BgAAsgz8662WvMeLZQelbjscdgvcQilAdp8CMVCQvRj3uXIzCsQGg1MqXPpjDrN6FHQLvQbyPeWXJAleodxB3Q/UlDkNzwOQU3rg941vNCIREdF48V8VIiIimhDWbunSvN64qx/xRNLgaKCzX+05UOpJL/TFUYf9Qe1EhI48gYISndKD8UwvyO5R0D2ORoYZYvlBRnWZEzarfhZCxvQaNXNCkoCPLJmS93giIqL9ZTnSF0BEREQEAG9s69G8jkQT2LirHyfPqdA9fuu+QWV7ek164oG4+M6MKwTSjQ9bhR4F2Qt/sUdBpvRA02DQYNHvEUoP4okUWoRyiewJCdm8TjsAbXlFfUVueUO2RTPKcNk5sxEIR3Hq/KqCpQpERET7i4ECIiIiOuJGRuOaVP+MvZ1B3UBBPJHCjlY1UDB/anrigVh6EByJKdu9gQiGI2rgoM6vXZCXZJUeyLKs6TdgtBh3F1thMZuQSKYAADvb1L4G/gKBAo9ORsF4AgUmScIZJ9QWPI6IiOhAsfSAiIiIjjgxzV8kZgGI9nQMIRZPL86tFhNm1qUzCsTFd2hYDQw0d2sbGRYXaZ+ViKUHQ8MxBMIxRKJq2YNR6YEkSUrZQ7ayAg0G9UoPpowjUEBERHSoMVBARERER5xeNgEAtPbqBwpe39qtbDfW+5TpAm7NFAI1o2BfV1DZbqhy53ye2MxQloGmtoDy2llkgUtoWpitzKMfECg0iUCvmWF9Ze61ERERHW4MFBAREdERJ2YUNI5lBwDAUDimKSEA0mUEqzd1Kq9Pmu1XtrWlB2pGgdg7QC9QYLeZ4bCrWQbbWwLK9v6OK8x8Xr7gAqDtbwCkyyf0sgyIiIgONwYKiIiI6IgTpwXMqvdpFvxtWeUHL6xrQzIlA0g/zT99YbXynkcno0CWZewTAwUGT+3F8oOmsTGNQOHpBaU6GQXl3iLdcYoiX1aA4ZPvn5H3eCIiosOFgQIiIiI64roHhFGEJcWapn7ZfQpae9RF/wdProPFrP46o9fMMBCOYXg0oeyfYhQoEEoB2vvU0YviyEU9er0I/AXOAYBp1W7UlDsBAB84vkYz9pCIiOhI4tQDIiIiOqJkWdb0KKgsdaCm3KmMP8zuX9A3NKps144ttDPEZoaxeArRWFIzvcDrshmWBGQ/4c8o1GtAr/SgUCNDADCbTLjhC6egPzhaMGuBiIjocGKggIiIiI6ocCSOkaj6xL+ypFjTXDAkjDVMplIYCEaV19kL8uwgQGgkhh6h/0F1mTawIBK/U1ReYMyhUenBeFgtpoI9EIiIiA43lh4QERHRQZdIprBmcyfWbe+BLMt5j93bqZYSOIsscBdb4RYW/CGhKeFgKIqU8HnZEwcsZhOcwujD4Ehc0yixutw4UFByoBkFOuMRK0u4+CciosmLgQIiIiI66P7zdhv+9I9tuPOJd/HGtu68x+5oHVS2G+t9kCQJrmIxUKBOPegXyg68ThtsVnPO52WPSBRLD2r8rpzjM0p0MgpMkqQbCBAV2Syo9asBiMY6L+ZNLcl7DhER0UTG0gMiIiI66B7+zy5l+//+sQ2nzqsyPHanMGGgsd4HQLvYDwulB2J/AqMn/e5iK7oG0tvBkZg2oyBf6YFORkGpxw6zqfBzla9esACrN3ViWrUHJ872w1Rg4gEREdFExkABERERHVSxeBJisUEiKSM4HNM0GsyIxpPYJ5QezJ7iAwBN6UE4EkdKlmGSJE2gwKhhoDgiMTis7VFQ49+/0oPx9hqoLnPi4jNnjutYIiKiiY6lB0RERHRQ7e0M5ux7Z2ev/rEdQSRT6bBCkc2sjEUUxxzKMjAyNt6wb0hd9BuNLfQKYw5busOIxpPK63wZBZ5iW04mQKHRiEREREcjBgqIiIjooNrZNpSzb1vzoM6RQEf/sLLdUOlW0vwddgvMJnXRnulT0D+O0gMxM2CHUNbgLrbBVZyb1ZBhMkmaRokAML3GY3g8ERHR0YqBAiIiIjqodrfnBgp6AxGdI4GufrXRYFWZOilAkiTNqMPM5IOegJhRoB8oEMccBofVRoj5yg4yqsu00wpOW1hd8BwiIqKjDQMFREREdFCJzQMzDAMFA0KgoFS7SHcXawMF0VgSA8Go4fEZek0JgfyjETNOml0BADCbJFz72eNhtfBXJSIiOvawmSEREREdNLIsYyA4mrN/eDSBkdEEiou0v3rkDxTYAKRLE0KRmOZYq8WEUqPSA50xhwBQU248GjHjgtOnYVqVG6WeIjRUuQseT0REdDRioICIiIgOmlAkjngipfte31AEU4rUxXc8kdT0HKjKSvvPLj0QAwWVJQ7DEYR60wuA8WUUmEwSTmj0FzyOiIjoaMZ8OiIiIjpoxIV/kc2sqfnPLj/oHowoYxTNJimn54BYehDOChQYlR0A6UaIdps5Z3/NOAIFRERExEABERER5RGLJ3Hboxvx3T+8jl060wyyiWUHZZ4iVPjU8YK9AW1JgtjIsKLEoUw8yHALEwqySw+ysw+y6ZUfMFBAREQ0PgwUEBERkaHVmzuxaXc/ugdGcN9z2yFnjQ/MJmYUlHmL4NcECrQZBYUyBMSMguBwTBNYqC7Nv+jPLj9wFlnyjkYkIiIiFQMFREREZOiVDR3KdnvfMJoKZBX0C1MJSj1FKBcDBUNZpQcFAgWlHrUUoS8wul8ZBT6XNijAbAIiIqLxY6CAiIiIdEWiCXT0DWv2vbS+Pe852tIDu6bvgDjaENBmFFTqBArEc3sCEUTjSeV1vh4FgDbIAAAfPbUh7/FERESkYqCAiIiIdL27dwDJlLbUYFvzYN5z+oRAQamnCF7hyX5wOKZsy7JcsPSgzKM//rDcWwSHPf/gptMWVsNuM8NmMeELH52D42eW5z2eiIiIVByPSERERLr2dOSWGQSHY0gkU7CYc581yLKMPqEPQZmnCF6hL0A4ElfODUXiGB5NKO/plRI47Ba4HFaEI3HN/tpxlBFUlRbjN18/HbFESjNmkYiIiApjRgERERHpGgxFdfcHwvr7B0NRzeK/uqwYHqe2V0BoJL3oFxsTOosscBss5su8uVkFdRWu/Bc+xmY1M0hARER0ABgoICIiIl2GgYJQTHd/a09Y2S5x2+EutsFmNcNhNyv7h4bTn5ndyFCSJN3PLNcJFIwno4CIiIgOHAMFREREx4hVGzvwq4c34N09/eM63ihQMGiQUSAGCuqFp/6e4tw+BYUaGWboBgr848soICIiogPDQAEREdExoG8ogvuf24F39w7gD09vRSKZynu8LMuaEoMyj13ZNgogtBgECrxC+cGQTqAg3wSDcq9D89pmNRWceEBERETvDQMFREREx4DNu/uRktMTDMKROPZ0BPMeH4rEkUiqEw+mVXuU7YBBoMAwo8CZP6Mg38Lf79NmFHx82VRYLfz1hYiI6FDiv7RERETHgJ1t2gkGW/YO5D1eDAbYLCbUCH0B9EoP1m3v0fQdmFLpVrY9WRkFyVQKPYPqdAS9iQcZ86aWos7vhATgw6fU49xTG/JeNxEREb13HI9IRER0lJNlGdubBzX7tu4bwCffP93wHLG8oMRtR6mnSPc9ABgZjeNP/9ymvK4td6KiRC0Z8GZlFPQNjSKZSmcrSAAqfNryApHFbMKPrliM8Eg8Z4ICERERHRrMKCAiIjrKdfSPKL0BMvZ0BhGJJgzOyA0U+Fxqj4Ls0oM9HUFEY0kAgN1mxlcuWACTMMUgu/RAHI1Y5i2CzapORdBjkiQGCYiIiA4jBgqIiIiOcpt29+Xsk2WgNxDROTpNDBT43HaUuIVmhuEoZFntX9AtlBFMr/ZoyhQAwOtUzx0ajo27PwEREREdGQwUEBERHeXW78wNFADG0wsAbR+CEpc2UBBPpDAiZCOIvQnEkoOMnIwCBgqIiIgmNAYKiIiIjmJD4Sh2t6uNDJ1FanuifIECcYJBubcIxUUWmE1qOUFQKGXoETITKktyF/5ikGF4NIGWbvWzKxkoICIimnAYKCAiIppkXt/ShWvvXIP7/7VDUwKgZ/OeAWSOKPPYsWhGufLegEGgYGQ0jpbukPJ6Vp0PJkmCq9iq7BMDBWJGQaVORoHXZdMEGfZ2BvMeT0REREcWAwVERESTSEqW8eALTegPRvHy+nbsaAnkPb5zYFjZnj2lBKUeoddAaFT3nB2tAWTiDy6HFTX+dM8Bb7FQQjASBwAkUyn0DamfU6GTIWCSJE1WgcjPQAEREdGEw0ABERHRJNLZN4xwJK68fmtHT97jB4Nq1kC5twilYlNCg4yC7c0BZXvOFJ8ywSC71wAAnVGH6hhFUZknd78k6e8nIiKiI4uBAiIiokmkqW1I+7p1yODItP6g+rS/1FOEEre6MDcMFLQMKttzGkqUbb1AQfeA2p+g1GOH1aI/6rDMmxsQKPMUwWLmryJEREQTDf91JiIimkSa2gKa12294bxNCQeEQEGZp0hTAjAQjOb0OAgOxzSNDOeKgQJN6UE6UNAzKE48MG5MWKqTOeD3seyAiIhoImKggIiIaBLJzigAgJ2tAd1jk6kUBkNq08FSjx0lQo+CaDyJSDSpOWdbs5pNUOK2a8YX6mYUDAoTD/JMMCjz5PYoYKCAiIhoYmKggIiIaJIIjcQ0jQMzxPGEoqFwDCkhY6DUUwS3wwqLWZ1AkN3QcOu+AWV7XkMJJEk91uPMnXrQPZh/4kGGXulBBRsZEhERTUgMFBAREU0SPYP6AYE+g0CB2J/A5bDCbjVDkiT4XPoNDWVZ1gYKppZqPk/MKBgaCxT0CD0K8i389ZoWMqOAiIhoYmKggIiIaJIwDBToZBkA2kCBuFA3mnzQE4igX5iSMHeq2p8A0PYoCI3EkUhqRyNWFuhRYDZJmn3Tqt2GxxMREdGRw0ABERHREZRIpnIaChoR0/wddouy3WuQUTAgLPpLhR4BJR79yQdb96n9CWrLnZrMA0CbURCNJ9HRN6yUNkhS/gwBu9WMj582FQ67BY31Plx98XEo9zKjgIiIaCKyFD6EiIiIDoVt+wZwx9/fhcthwXc+d6LuZACRmFEwf2oJ1u3oBZAOCCRTKZhN2vi/WJIgfrZm8oEmUKCWHWRnEwCAu9gKCUAmrCE2VizzFMFqyf/84fzTpuH806blPYaIiIiOPGYUEBERHSH/fL0ZkWgCvYFRPPRiU8HjxQkDYv+AlCxjMJg7IrFFGHNYW+5Utkt0Sg9SKRnbhYkH2f0JAMBsMsHjUrMKNuzqU7bzNTIkIiKiyYWBAiIioiMgmUphi5Dq//aOXk0ZgJ4eofSg1u+EV1i092b1KUimUmgVAgUNVWo/AG2PgvR53YMjGB5NAEiXEcyu9+lew7Qqj7K9Za+ageDP05+AiIiIJhcGCoiIiI6Alu5wzr5XN3YYHh+OxJWFPJBuHOgXavyzJx909o8gnkgBAEyShDq/mFGQ26NA7HNQ5inS9EAQTa/x6O73+/KXTRAREdHkwUABERHREbCzNZCzr7U3N3iQ0dE3rGwX2cxwF1tRLizOsycfNHeFlO2aciesFrPyWiw9GB5NIBpLojegnp+vKaFhoICNCYmIiI4aDBQQEREdAXqBgr6A/phDANgm9A+YVu2BJEmaqQTBkZjm+OZuNVDQUOXSvOd12mCS1FGFg+GoplFiRZ5+A9OqPZB09ucLLhAREdHkwkABERHRESBmCGQYjTkEtIGCuQ3piQSeYrVHQXBYDRTIsqw5fkql2p8AAEwmSdPfYDA4qvnufIt+h92CGqExonoOSw+IiIiOFgwUEBERHQGB4VjOvpFoAiOj8Zz90VgSu9vVUYSZ0YUep1XZJwYKWnvCaO9VAxGLppflfGZp1ojE8QYKAKBxik/zuthuQXGRVf9gIiIimnQYKCAiIjoIdrUPYf3OXiRTqYLHjsbSfQH09OqUHzS1BZBMyQAAh92MqWMTDDxONStgSAgUvPZul7I9o9aDytLciQQleQIFFQUCBfPGMhoyyr3MJiAiIjqaMFBARET0Hu1oGcTPHngbtz++Gf9+s7Xg8UNhdVEvSUB1mbqQ1ys/2CqUEcyuL4HZlP7nW1N6IPQoWN/Uq2wvm1+lew3i5IOWrhBiCTXAUaiMYPYUbaAgGtcPehAREdHkxEABERHRe/TQi03K9qMv7y54fCAcVbY9Tpsm1T97egGg358ASDclzIjFUxiNJZBMpdA/pH7+nKyn/xliRsEOobGis6hwGYHLoX2/nI0MiYiIjioMFBAREb0HsiyjpVs71rDQE3axTMCbFSjoHdJmFIQjcbQIow7FQIGr2KqZQBAcjmEoHENKlpV9pR797IBSjxooCEfUvgjjnV5w6Ycble2PLW0Y1zlEREQ0OViO9AUQERFNZmLTwIzmrhAa632G5wSE0gOfyw6/UOOfPSJxR8sgMst+T7EVtX514oDZZIKr2IrQSHqhHxzWNkJ0OaywW8261yBmFIjyjUYUnXFCLSpKHHDYLZhR4x3XOURERDQ5MKOAiIjoPXh370DOPnFCgZ4hofTA67RpnvqLZQnpzwoq23MaSiBJkub97IaG/UE10CBmDWQzChSMN6PAJElYMK2MQQIiIqKjEAMFRERE78GezmDOvl0FAgViRoHXZYfXJSz2swIFrb1qWcPUKk/OZ2U3NBwICYECt3FTQp/LDkln/3gDBURERHT0YqCAiIjoPejsyy090CtHEA0Nq8EAn8umaUoYGolrRiy2CYGCOqHsIEM8Nzgcw4DQyLDMoD8BAFjMJriFczMYKCAiIiIGCoiIiA5QIplC18BIzv7+4ChSKVnnjDRxPKLXaYfXpZYByFB7DQRHYppj6ypcOZ/lyQ4UiBkFXuPSA0C//KCCgQIiIqJjHgMFREREB6hnMIKkTkAgmZJzeg1kyLL2PZ/LBrvVDIddbTqYyTho71GzCVwOqyZ7ICM7UKDpUZCn9CD9vjZQYDZJhr0LiIiI6NjBQAEREZGgqS2AZ17bh4HgaMFjO4Syg6rSYniKrcrr3kBE7xRs3TeI4dGE8jqT6u91qgv0TA+DNqGEoc7vzGlkCGh7FAyNxDAQHF/pAZA7OrHMWwSTSa9zARERER1LGCggIiIa0xeI4Ja/bcDjq/bgd3/fDFk2Lh8AtIGC2nInyrxq2n7fkH6g4YV1rcr2ohllSkaAmC2QaWio6U+gU3YAaDMK+gIRhCPqiMR8Uw8A4PSF1RBjD7XluT0QiIiI6NjDQAEREdGYVzZ2IJ5INxLc2xnCvq5Q3uPbhUBBdbkTfp/6hF4vUBAaiWHT7n7l9QdPrlO2NZMPhtMZBWJWQnWZ/iLe41SzGMRpCmaTBJ8rf6CgocqNG/9rCRZOL0Od34WPnzY17/FERER0bLAc6QsgIiKaCFKyjDe2dmv2rd7UiWnVuSMJMzr6tRkFYgPDPp3Sg46+YWSOcDmsmDe1VHlPXNRnGhj2BtRgg9+rX0Yglh6IxltGUF3mxLc+fVzB44iIiOjYwYwCIiIiALvbh3KyAN7Y2q0ZVShKJFPo6lcnHtSUO1HuzZ9R0ClMSKgqK4ZJyPsXSw8C4SiSqRQGQ2q/gXKDaQQenQaHgHFggYiIiKgQBgqIiIgA7GwN5OwbiSY04wlFvQF14oEkpZsZlmtKD3IzCsTAQlVpseY9sfQgOJxuSpgSeiSUGfQbsJhNcBblJggaBRaIiIiICmGggIiICDDsRyA+1Re1CxMJKkqKYbWYUC40MxwIaRf6ANAtZBRU5wQKtFMPxIwEn8sGq8UMI3pZBeXMKCAiIqIDxEABERERgH2dQd39RoGC7P4EADTjEWUZGBYmEABAlxAoqMwOFIhTD4ajmkaGYgBCj16fAj8zCoiIiOgAMVBARETHvOBwDP1BNSBQ51cnDBgGCoSJBzVjgQKH3QKz0EAwNKIGChLJlKY5YXbpgdjMMJGU0dqtjkYslB2gn1HAQAEREREdGAYKiIjomCeWHXicNkyvUScdDIYNSg80gYL0ol+SJLiFrILQiNrfoDcQUUoRTJKEihLtQt5ZZIHFrAYZdnUMKdti7wM9uoGCAucQERERGWGggIiIjkovvt2Gb92+Gn96Zisi0UTeY/d1qWUHU6vcmqf7ehkFI6MJdAg9Cuor3Mq2WygDEDMKmrvVYITfVwSLWftPsCRJmvKDZiF4UbD0ICtQ4CyywO2wGhxNRERElB8DBUREdNQZHo3jby82YWg4hjXvduHnf3kH0VjS8Pg2YdE/pdKFEnf+QMGu9iFk2hQ6iyyoLlPLCIwyCppa1QyBGbVe3evwOPUnGxTqNyD2RgCAM0+shSSMXiQiIiLaHwwUEBHRUWf9zj5ldCEAtPWGsaN10PD49l61H0Cd34USt5q2H9AJFDS1BZTtWXU+mIRFuVFGgTh+sbHep3sdPlduCQGQ288gW0OVmtFQX+HC+adNy3s8ERERUT65g5cnqGeffRZ//etfsWXLFsiyjPr6epx77rlYsWIFiory12GGw2FccMEFaG1txYsvvoi6urqcYxKJBFauXImHH34Y+/btg8ViwcKFC/GlL30JS5YsOVQ/FhERHQJvbe/J2WfUlDCeSKJ7QJ0wUOt3ISUEGQbDUciyrHlCvyPPol+bUZAOFIQjcU1PA6NAgTgiMcNuMxsGEDKmVnnwXx+bi/beYZyzeEpOWQMRERHR/pgUgYLbb78dd9xxB8xmM0466SS4XC5s2rQJt912G/7xj3/gr3/9K7xe/TROAPjRj36E1tZWw/dlWcZ3vvMdPPPMM/B6vVi2bBkCgQBee+01rFmzBj/96U9x0UUXHYofjYiIDrJINIGt+wZy9g+FYzpHA539I0qTQYtZQmWJA6NCmUI8kcLwaAKusZr/RDKlGaWYGygQMgoi6e8UMxA8ThsqS/RLCbw6TQmrSovHVUawbEF1wWOIiIiIxmPCBwrWrVuHO+64Ax6PBw888ADmzJkDABgZGcE3vvENvPrqq7jttttwww036J7/1FNP4emnn877HStXrsQzzzyDuXPn4t5774XP5wMArF27FldddRV+/OMfY9myZaiu5i9hREQT3b7OoKbsICMwrB8oaBPKDqpKnbCYTXAWSbCYTUgkUwDS2QiZQEFX/wgSyfTnm00SplS6NJ+nl1HQ1qN+x4waj+HC36uTOSD2PyAiIiI6HCZ8buLf//53AMAXv/hFJUgAAMXFxfjGN74BAHj55Zd1z21tbcWPf/xjzJ8/H8XFxr9o3X333QCA66+/XgkSAMDSpUtx+eWXIxqN4oEHHniPPwkRER0O4qhD0ZDRmEOhkWFdhRNAegKBmO4fFJoSagILZcU5af5uh9ijIH1ez2BEc44Rn04zw0L9CYiIiIgOtgkfKPjxj3+Mf/7zn/jsZz+b814ymU4NNZvNOe8lEglcc801SCaTuOWWW3SPAYBdu3ahpaUFZWVlOOmkk3LeP+eccwAAL7744nv5MYiI6DDZKwQKSj3qwnvIIKOgQ+gdUFvuVLZdwnjBsNCUUOw1UOfXZhMAgMepnhccO687oAYKKkuMF/56GQUMFBAREdHhNuEDBRaLBTNmzIDH49Hs7+zsxM033wwAuPDCC3POu+OOO7BhwwZcd911mD59uuHn79y5EwAwa9Ys3VTQzP6WlhZEIpGc94mIaGIR+wccN7Nc2TbKKOgWn/aXCoECoYQgHFEDBWIZgRhYyBB7FIRH4kjJMnoGRpR9Rv0JAGjGMmboBSOIiIiIDqUJHyjIdtNNN2H58uU4++yzsXnzZlxxxRX48pe/rDlm3bp1+MMf/oAzzzxTNxNB1NOT7oxdWVmp+77dbofH40EqlUJfX9/B+SGIiOiQCI3E0Dc0qrw+XgwUDMcgy9reBclUCr0BMVCgLuLdDrHXgJqNIGYU1Pr1AgXqeSlZxkBwVMksAICKPBkFPpcdJ8/2AwCsFhM+sngKanSCEURERESH0oRvZpht5cqVCAbTT4tsNht6e3vR19eHiooKAEAwGMS1116LkpIS/OxnPyv4ecPD6V/48o1YtNvtmmMPFqvVDL/fXfjAYwD/O9B48D6hQkLRlLLtddlwysIa4JGNAIBEUkaRswgeYbJAZ9+w0vhQkoB5sypgs6ZL1SrKXAC60+fK6ftvZDSuCUQsml0Jf5l2IV8uy7CYJaXhYfeQmslgs5oxc2oZTCbjKQY//OJSDEficBRZYc5zHB04/l1C48H7hArhPULjMVnvk0mXUfDUU09h48aNePTRR3HiiSfi6aefxvLlyzEykk7r/MEPfoCOjg787Gc/Q2lpacHPy/QuGM/oKSIimth6A2qKf025Cy6HFVaL+k/dYGhUc3y70JjQ73MoQQIAmoBCcKy/gVh2UGQz62YHSJKEUo8afH53T79wTc68QYLM+a5iG4MEREREdMRMuoyCzIjCRYsW4Z577sFFF12EnTt34pFHHoHL5cJzzz2HSy+9FB/4wAfG9XlOZ/pJ0OjoqOEx0Wj6aZDDYVxXeiDi8SQCwi+1x6JMhK23V79LORHA+4QKy9wjze1Dyj63w4K+vjA8xTb0B9N/x+9tHUSxWV2A79yrLuL93iLNPSbJanZCfyCC3t4QduxRS9AqfA7096uBA5G72KpMOti4s1fZX+q28z4+gvh3CY0H7xMqhPcIjcdEvE98vmJYrfpN/rNNukCByGaz4aMf/Sh27tyJrVu34vnnnweQ7jtwzTXXaI7NBAJ+/vOfw+Fw4LOf/SxOPvlkpTdBb28v9ESjUQSDQUiSBL/ffwh/GiIiMpJKyWjvG4bDbka51zhoOxBS0/xL3emn+j6XGijIbmjYPagGayuypgtoexSMTS8QGh9mHy/yudSmhF1CI8OKPI0MiYiIiCaKCR8o+O1vf4s9e/bguuuuQ1VVVc77Nls6NTSRSCjlB//+978NP++FF14AACxbtgwnn3wyZs+eDQBoamrSPb6pqQmyLGPKlCkoLuaIKiKiw21HyyD+8PRWDIaiMJskXLv8BDTW+3SPHRQCBZkJAl5h0R4Ia0ckdguL+KqsMgLN9IJI+ryewfFNLxADBSIGCoiIiGgymPCBgjVr1mDDhg1YuHAhrrzyypz3X3nlFQDAwoUL8atf/crwc04++WSEQiG8+OKLqKurU/Y3NDRg6tSp2LdvHzZs2IDjjz9ec95zzz0HADjjjDPe+w9DRET7ZSA4ijse34zh0QQAIJmS8c7OXsNAwYDQg6DUk16siyMHxUACALT1qk1qq8q0gQJXVkaBLMtKOQGQf9Hvc9l091fmmXhARERENFFM+GaGl1xyCQDgjjvuwKZNm5T98Xgct9xyC958802UlZXhoosuOuDvuOyyywCkGyGKIxDXrl2L+++/H1arFVdcccUBfz4RER2YB19oUoIEGdmLfc17QTGjIF16UGoQKAiEoxgaVjMMplRquxK7hDGHyZSM0VhSU3qQb9EvBidE+bIQiIiIiCaKCZ9RcP7552PdunV4+OGH8ZnPfAYnnHACPB4Ptm3bhq6uLvh8Ptx1113weDwH/B3Lly/HqlWr8PLLL+Occ87BkiVLEAqFsG7dOsiyjJtuuklpokhERIdHJJrAxl19OfsHw/qBgkg0gZGoGlTILNa1GQVqxkFLt9pcyOeywevUZgG4iqyQAMhjr7sHRxCOxJX382cU5AYKrBYTfAYBBCIiIqKJZMIHCgDgJz/5CU499VQ89NBD2LJlC2KxGGpqanD55ZfjyiuvVBoSHiiTyYQ77rgDf/nLX/D4449j9erVcLlcWLZsGa666iosXrz4IP0kREQ0XjtaAkim5Jz9AYOMgr6A+rTfbJKUhb8YKBCbHTZ3qxMLsrMJAMBkkuB0WJXgwJ6OoPKe3WrOCSyI9AIFFT4HTBzFS0RERJPApAgUAMC5556Lc88994DPX7duXd73rVYrVqxYgRUrVhzwdxAR0cHzrjC6sKbciY6+dD+BQDgKWZYhZS26+4fUQIHPZYPJlH5fDBQEwzEkkilYzCa0dKkZBQ06gQIg3acgEyjY3a4GCvw+R873i3QDBSw7ICIiokliwvcoICKiY9OWvQPK9ukL1fKvRFJGSCgByOjsUxsTZvoTpLfVRbsMIDjWl6BZKD3QyygAALfQp2BPx5Cy7fcV6R2ucNjNsFm1/8QyUEBERESTBQMFRER0WCRTKTy1Zi/ueXornn2jGZFowvDY4dG4pnHgyXP8moW3XvnBf9a1Ktt1fqeybbWYNRMMBkJRRKIJ9A2p/QqmVLp0r6PMqwYExOsR9+uRJAnVpU7NvikV+sEIIiIioolm0pQeEBHR5Pbk6n145rV96RdbgL7AKD5/zmzdY/uFRbzVYkKZpwglLruyWB8MRTVZALtaA9jePKi8/sDxtZrPK3XblRKCwVAUYtWA3Wo2XPiXG+4vnB3w6TNn4JGXd2NkNI65DaU4ZW5FwXOIiIiIJgIGCoiI6JAbDEXx7zdbNPt2tgYMjx8QxhyWeoogSRJK3EKgIGvywaoN7cr2jFoPGqq0T+9L3Ha09KSbFw4GRzXZDDXlTsMmg0YBgTJP/owCAJg7tRQ3fKG04HFEREREEw0DBUREdMg9/1YrYomUZl9/cNTgaO17ZZ50jwFxtGB26cFeoX/ASY25T+5LhIX9QCgKWQhE1PqdOcer371/mQZERERERwMGCoiI6JBrag/k7BuNJTEymkBxUe4/RQMhNVBQOrZYLxEmCQxmBQpautSJBHUVuQt/zYjErIyC2nLjQIFh6UGBZoZEREREkxmbGRIR0SGVkmW09Q7rvjdgkFUglh5knuqLGQVi6UE4EtccX1ue25hQXPD3DY2iTZiQkC+joFQno6DIZkaxnXF2IiIiOnoxUEBERIdUXyCCaCypvPa6bMq2UfmBuL90rPRAzCgQSw86hEW/s8gCn/D5GWIJQUt3GEPhmPJaL7CQYbWYcj6v3JvumUBERER0tGKggIiI9ossy/jbi0248f51mkkDRlp71IV8hc+BmjL1Cb5xRoHYo2Cs9MCtX3rQ3htWtmvLnbqLeDGjICXLynaxXT+woD1X29BwPI0MiYiIiCYzBgqIiGi/bG8J4N9vtWJPRxAP/HtHweNbe0LKdl2FS7PQ7g9Gc45PplKaQIBeoGB4NIFYPJ2loC0j0M8O8LnsMJtyAwi1fv3Agsjv0wYKjptVnvd4IiIiosmOgQIiItova9/tUrY7+0cQCOcu9kWtPeoT//oKl1JKAOhnFAyGohAe+isBAo/TBnFJn/nerv4RZV+NQWNCk0nSBBoy8jUyzDjzxFoU2y1wOay44ty5+MBxNQXPISIiIprM2I2JiIj2S3ZfgZ2tASyeW2l4fKewkK8td2omDuj1KNi2Ty1nKPXYYbOaAQAWswkepw1Dw+n+AoOhKCpKitE3FFGOz376Lyr3FqFvSPt9RhkIopm1Xvz666fBbDbBxN4EREREdAxgRgEREY1bMpXCno6gZt+O1oDh8SlZ1izkK0uLUSr0C9DLKHhze4+yfcJMv+a9kqzJB6mUrJl4YDTOENDvLTCejAIAsFrMDBIQERHRMYOBAiIiGrfWnjCi8aRm3848gYKhcAyJpFpHUO4t0kwvGBqOQRbqDEIjMU1GwSlzKzSfJwYKAqEYAuEokin1/HyNBst0ggg1eUYjEhERER2rGCggIqJxy84mAID23mGlsWC23oCaTeByWOGwW+BxqlMGEkkZI0IpwrbmQWUqgddlw8w6r+bzfFmTD8RSAq/LBrvNbHjtdVllBh6nDZ7i/BMPiIiIiI5FDBQQEdG4dQgTBkQ9QkBAJAYK/L70E31nkUUzgWAoHFO2xX4Gs2q9Oen+YjbCYDiq6XHgLynOe+3HzyrHWSfWwuWwwiRJ+PiyqXmPJyIiIjpWsZkhERGNm7iQF3UPRHKe2APZgYJ0o0FJkuBx2pQRiMHhmDKtoGtA/fyqstyyAG3pgTajoLJAoMBiNuHSD8/GJR9qhAyw5wARERGRAWYUEBHRuHX2qxkFFrP6T0jPoH4AoTcgPPEXJhKI5QeZKQbZn19dlrvwzy496BcnHpQYTzwQSZLEIAERERFRHgwUEBHRuESiCQSEMoHjZ5Ur22ImgEiceCBOJPAKgYLgWKAgJcvajILS3ECBWHoQCEc1gYhKneOJiIiIaP8xUEBEROMilh3YbWbMneJTXncP5vYoiCeSmnMKZRQMBqOIxVPKft1AgZBRkEzJ2NelNlesKFB6QERERETjw0ABERGNi6YsoLRY8wS/W6f04B9rmxGOxAGk+wGIPQz0Mgo6B9TPL3Hb4bDnttFx2C2ayQaRqDptgaMOiYiIiA4OBgqIiI5h7b1hPPt6s+E0A5GYHVBdVqxpHjgUjmE0po45jMaTeO6NFuX1B0+u02QR6GUUiJ+vl02QIZYfZFjMJlTrND8kIiIiov3HQAER0THq+XWt+NGf38KjL+/GLX9bj3gimff4gZC2MWGJxw6LWW0K2C9MIOjqH0EskS4jsFpM+MTp0zSfpZdR0JUViDAilh9k1FW4YDbznzQiIiKig4G/VRERHYP6hiJ46IUmJFMyACAQjmFPRzDvOYGxcYZAerFukiS4i9UFf6bMAAB6hLGIFSWOnDICTaBgJJNRIE48MM4O8OlkFNRXuvNeOxERERGNHwMFRETHoOauUM6+na2BvOeIEw8yi3W3w6rsC40IgQKhZ0GFL3dsoScro2A8Ew8y9DIKplQxUEBERER0sDBQQER0DGrX6UlQKFAwGFYzCjKBAlexECgQMwoGtRkF2cSMgmRKRv/QqCYQsb+lB8woICIiIjp4GCggIjoKxBMpvLOzd1xNCQHoHrerI4hkKqVzNBCJJhCNqT0MfGOLdZeQURAeURf6vWLpgU5GgcNu0fQ32NESULbtVrPy+Xr0Sg+mMFBAREREdNAwUEBEdBT423+acMfjm/GT+97SpPAb0QsURGNJtPaEdY8PCNkE6d4E6QCB2KNAU3qg6VGQmx0gSZImq2BH66CyXVVaDJMk5ZyTkZ1RMLPWi7oKl8HRRERERLS/GCggIprkUrKMl95pBwDE4ik8vmpP3uOTqZQmmGC1qP8U7OvM7V0AaPsTeF02ZSEv9ijINDOMJ5IYDKqBBb9O6QGg7VMgZhTkKzsAgPoKpxIsKPcW4WufXAApT2CBiIiIiPaPpfAhREQ0kbX3arMDNjT1IZFMwWIwLrBnMIJEMj3tQJKAJXMrsXpzJwBgn06TQ0CbUeBzqQt8t9ijYKz0oDcwCnlsn9kkocyjX0bgEbIR+oTRivkaGQKA1WLGdy89ETtbAzhhlj9nogIRERERvTfMKCAimuSymxAmkils3t1veLxYXlDhc2BmnVd5rTcNAcgOFKgLf5dYejCWUdAtZCuUeYtgNun/U+MVAg4iveaH2cq9DixbUM0gAREREdEhwEABEdEEEokm8H//3IY//3MbItHEuM7Rm1awtytoeHxT25Cy3VDlRoPQCLCtN4x4IrehYSCUOxoR0DYzzPQo6OhXMxxqypyG1yGWHoiMShWIiIiI6PBgoICIaAJ5cvVerN7UiVc3deKFt9vGdc6u9qGcfWIqf7YmIbAwu96HWr9TmUCQTMlo78ttaDgYUj/PqPQgHIlDlmV09qsZBVV5+g14nfolCXpTEoiIiIjo8GGggIhogkilZPz7rVbl9T9e21fwnJHRBAZD0Zz9fQH9QMHIaFxTetBY74PFbEKdX50a0Naj7XkQjSexZZ86lUCcYiA2M4wnUojFU5pAQb7GhHoZBQ67RZOlQERERESHHwMFREQTxI6sEgLJVLiTf/eg/ijE3qGI7v5d7UNKo0GXw4rq8nRpQJmnSDkmFIlpzlm3vUcpg7DbzDhuZpnynjNrUR8aiaFznKUHXp1AQYXPwQkGREREREcYAwVERIdI18AINuxKTyAYj3XbezSvo7EkRkbz9ykQAwViY7+hcAzxRDLn+D0dau+CWXVeZcyhuOAPj/UayFgzNhEBSE9IKLKp32Mxm1AsfG9rTxijMfV79zejgP0JiIiIiI48BgqIiA6B3kAEP/zTG/jtY5vw8Iu7xnWOuIjP0OsXIOoeUDMH5jWUKAt/QL9PQYdQFlBfoZYbaMYcRtRAQSolY0+nel3LFlTlfKZLOHd7S0DZ9jptKC4yLiMQxyNmVDJQQERERHTEMVBARHQIvLW9B4lkOsn/xXfaMBA0bi4IAClZRufAcM7+tt7cfSJxFGF1eTFKPWqDwH6dQIFYFlAtlAW4DDIKegMRxOLpjAgJ0ExIyCj3qmULm3b3CZ9vnE0AAA67GVaL9p8hNjIkIiIiOvIYKCAiOgTasxb4hSYYDARHlQW5qK03f0ZBlxAoqCwp1izae7MCBclUShtYEBbymkCBkFEgNj6sKHHAbjPnXEOl0Nywe1DNcMj0PzAiSRLmNZQor8s8RThxtj/vOURERER06FkKH0JERPurPWuBv/bdLnz6zJmGx3f16zclbO8xDhTIsqxZmFeVFqPc6wAQAAD0BbQNDfsCo0qWgzR2fIZR6YEYqKgTShVERuUC1aX5MwoA4MufWICNu/vgdlgxs84LqyU3EEFEREREhxcDBUREB1kimUJ7nzajYGg4hnAkbjj6T+wdYDGblAaIbb3DkGVZdxJA92BEmUYAAJWlxSgTMgoGssYmdghlB+W+Itis6qLc5VD7BYRH1KkHYkZBvd8gUGAQECiUUQCkpygsnltZ8DgiIiIiOnxYekBEdJB19A0jmZJz9htlDQDa3gGnzFHT70eiCQxmLfgzXl7frmzXljvhclg1IweDw9oxh+L3V2eNLRQbEo6MJpAau35NoMAoo8AgUJBvNCIRERERTVwMFBARHWQt3frlAmIwIOc9IQNhZq0XJW61KaFeQ8NoLIlXN6ljC888sRYA8gYKOvrERobaxb1byHSQAQyPxhGNJzWTE2r9+gv/cm+RZtoCABTZzPC5cqcaEBEREdHEx0ABEdFB1tGnHxDoHNDPKEilZLQIT+5ryp2aRXl2vwMA2LpvQCk7KLKZsXR+emyhRwgUDGUFCsTsgLqsMoIimxlmk7rYD0fimiCB2SSN9T/IZTGbUO4r0uyrLivWLZcgIiIioomPgQIiooNMzBxwFqmtYIxKD1p7whiNJQGkF+RTqzyahbze5INtLYPK9oLpZXDY098jBgrCkbjS6yC7b0J2GYEkSZryg9BIXNMMsdRjh8lkvPDPblyYXdpARERERJMHAwVERAeZmDlwQqPab6DDoPRgZ2tA2W6ocsNuM6NOk1GQe972ZvWcuVN8yrYYKADSC35A2zfBbJJQo9No0J01IlHMKDDKJsj48OIpSlDEYpZw1ol1eY8nIiIioomLUw+IiA6ieCKFXuFJ/Imz/Fg91kugNxBBPJGC1aKN0YqBgsZ6HwCgskR9Qp89vSA0EtNkGcxpKFG27VYzimxmJUMhOBxDiduuKTuoLXfCYs6NE7uyAgXiz+HPKi3INrehBL/679PQH4yixGWH3cYxh0RERESTFTMKiIgOop7BEchjAw/MJgnzppYgU6ovy8BAcFRzvCzLaGoLKK8zgQKvS7+EAAB2tg4p216nDVVZaf96fQrEBov1lfrTC1zF6nmhkdh+ZRQAgNViRlVpMYMERERERJMcAwVERAdRp9CHoKLEAZvVDJ9LnWDQnxUoCI3EERwrDwCAGTUeAIDXadccNxRWGxO29oSU7Zm13pymgR6dyQfiOVMq3LrX7hZ6FASHtT0KspsVEhEREdHRi4ECIqKDSOxPkGnoV+ZRF9nZgYIeYTFebLfAPfZU32oxaRohBsJq+YEYjNDrNaAZkTgSyz3HYMxhiRDQGAxH0StkFPjHkVFAREREREcHBgqIiAro7B/G82+1on9otOCxbUIvgExJQJlXCBRkfUbvoNAHoES7GBczEQJCRoHYFFEvUJCdURCJJjSjErMnFGSUuNXva+8NK+MXAaDcx0ABERER0bGCgQIiojxCIzH8/C/v4KEXm/CrRzZAzjQg0CHLsqYx4bTqdIp/qUddgA8EtY0JezQNA7MDBWKvgfR5yVRKM2ZRN6OgWNujoEvIcrBZTfC57TnnANDsFzMQbFYTPEJZAhEREREd3RgoICLK4+nX9iEcSfcQ6Owf0TT4y9Y1MKJ5cj97SnoaQb7SA3GyQEVWoMCrk1HQGxhVxhxKElBVmvukX9PMMBzVBAqqSophyuppkFFqEEAo9zpy+iAQERER0dGLgQIiIgOhkRheXt+h2SdOD8i2vSWgbNf5Xcq4wfH2KKgoyQ4UqAv+TI+Cjj617MDvc8BqyZ0wIGYw9AdHNRkIVWX6ZQeAttRBVO5lI0MiIiKiYwkDBUREBvZ1hTRjCQHt9IBsO1oGle05U3zKthgoGAhGkRLKFzQ9CrIW5D5h8kFm6oEYKKgp029KKI4yHAhGNT0Nskcpihx2C4p0RhuykSERERHRsYWBAiIiA+KkgYzWHuOMgr2dQWW7sd6nbJcKgYJEMoXQ2DjEaCypKVXIaWboFgMF6WvpHiicHSA2T0ymZGxvVgMY+QIFgLahYQZHIxIREREdWxgoICIyEBQW8RlGgYJINIHegFpWMKXSpWwXF1ngsKujDjOTD9r61M+ymCWUurULcnHMYWDsWrqFDASjRb/datb0KRgeVacX5Cs9AAwCBcwoICIiIjqmMFBARMeMeCKFt3f0ontwpPDBUNP9RX1Do5qxgRntvWp6v91mzhknKDYKzGQqbNunPumfXu2ByaRtGChOPQgNx5BMpTTXXllivIDPLmMA0s0PjcoVMvQCBX5mFBAREREdUxgoIKJjxp+f3Ybf/X0zfnLvW+gZR7AgoJNRAOhnFYi9C+r9rpzJAuKifzA0FigQSgLmTS3N+Uxx6oEMoGsgopQtAEBFiXF2QJlOoKCypBg2a24PAhEzCoiIiIiIgQIiOibIsozXt3QDACLRJP78z+0Fzwnq9CgAjAIF6r66ClfO+z6XNqMgFk+iqW1I2Td3aknOOXarWVOy0NQa0LwnBh+y+X25i3u968rWWOfL2VdcZMk9kIiIiIiOWgwUENExIfMUP2NHawDtwgQBPZpGg0L6vd7kg9ZeNVBQrxcoyCo92N4yqExUsNvMmFbt0b0GMRiwQwgUVJQ4IGVlLYj0RhrW+/OXHQDA/GmleP9xNeprnQAGERERER3dGCggomOCOCIw4909/XnPEQMFC6aXKdvZGQUpWUZbj/r59f4CGQWhKFZv7lJez59aCotZ/69jsaHhTiFQkK8/AYCcHgnA+DIKJEnCZefMxqfPnIlT51Xisx9sLHgOERERER1dmE9KRJNKJJrAms2dcNgtOG5mOVwO67jO6+zL7UnQNWDcpyAaS2I0llReL5xWhpfeaQeQblyYSslK88G+QATRuHpsrc6TezFQ0No7jOGI2mvg9IXVhtchnidmRVQWGHPYWOdFiduuOUcvgKHHZJLwkSVTxnUsERERER19mFFARJPKQy804cEXmvCnf2zDt+96DS3duWUAevQyCrr6jQMFQ8PqAluSgDkNPmQy/WMJ7fQBMcOgwufQ9BXI8LnVzIDgcAzJlAwgnTGwcEZuI0PlPFduc0HAeDRihtVixjWfPR4VY5kFc6b4dBscEhERERFlY0YBEU0q21vUSQGjsSSeXL0XX79oUcHzOnX6EeTLKBDLDtzFNhTZLKgqLUbnWHChvXcY1WOjBgs1MgSAEoMF/4mNfphNxjFbr0HDQr0+CNmqy5y48YtL0NoTRn2FK29PAyIiIiKiDGYUENGkEU+k0D80qtm3oakv74IfSE886NDJHhgajiESTege/+83W5XXmT4B4phAMaVfDBQYLeA9Thv0lukNVe68164XKDCbJCVIUYjFbMK0ao9hDwQiIiIiomz8zZGIJo2eQARy1j4ZwKoNHXnPa+0JIyz0BBDpBRnWbunC2zt7lddlnnTKfolQPjAY3r9AgcVsgtuZu+hvqMwfKPA5czMRqsqKYbXwr28iIiIiOjT4myYRHTGyLOOl9e149OVdhgt5UbdB5kCLzrhC0fqmPmV7Ro0HUyrVxbxen4INu7TTED56arqxX/bkAiDdXLFPyHLIN1kgu/zAbJJQU54/M0Avo2C8TQmJiIiIiA4EAwVEdMRs3N2PB/61A8++3oKHXmgqeLwYKBDL7XsGI3nPWy9kBxw/q1zTCFAvo2Bvx5CyfflHZmNWnQ8AUOpRmwEOjAUK2nrVbIIimxnleRoGzqj1aF77XPaCmQF6zQzH05+AiIiIiOhAMVBAREfMs683K9trt3QhJowY1CMu6o+bUa5s9wdHEU+kdM/pCUTQIpQGnNjoh9+n9hoYCGl7HgTCUfQH1bKCRcL36GUUZDcyNOVpGHje0qma11ML9CcAAIfdguoy7YSDqdUeg6OJiIiIiN47BgqI6IiQZVmZIJCxZe9A3nPEjIL500qVRbksA31D+lkFr7/bpWxXlxWjusypNCcEgKFwTHP8no6gsl3itqPEbde8zhgIRSHLMtrE/gQFSgJK3HZc9IHpyusl8yrzHp/x9YsW4f3HVWNatQcfPLkOc6b4xnUeEREREdGB4HhEIjoiOvtHcvoSvLOzFyc0+nWPl2VZk1FQU+5EubcIPYF0gKB7IJIzCUCWZbwmBAqWLagCkJUZkCdQMD3ryb0YKEgkUxgeTYyrkaHo3FMbUFPmhNlswsLppQWPB4Cq0mJ84aNzx3UsEREREdF7xUABER0RetkD7+4dgCzLkHTS9weCowiOqIGF2nInKkodaqBgMLfXwO6OoPK+BODUebmBgqHhqOacXe1qf4LpNdpAgbPIAqvFpJQ59A+Noq13WHk/XyPDDEmSDIMhREREREQTAUsPiOiI2NsZzNk3NBzDYCiqczTQ1BpQtks9dnicNlT61Np9vYaGYjbBnIYSlI01GhQnCYRG4kgk0wv/eCKpySiYVe/TfJ4kSZrJBU1tAUTH+ipIAOr8+ScYEBERERFNBgwUENERIabsi8SFumhXW0DZnlqVftJfUao2JczOKIgnUnhrW7fyeun8KmXblzVyMDgcU747EzSwWU26zQbF8oNtzYPKdpm3CEU2JmkRERER0eTHQAERHXbxRFLTyLBWeBK/RyfTAAB2CRkFmQW8OL2gb0g7vWDznn4MjyYAADaLCSfNVtP9rRYziu3qoj7Tp2BHi/odM2u9sJhz/4os8aiBAvF48VqIiIiIiCYzBgqI6LDr6BtBSpYBABazhGXC0369jAJZlrMyCtKBglLh6f7g2BSCjCbh+EUzy+Gwa5/2i+UHQ+F0ucMOIRgxO6vsIMPvVQMCI9GEsl0+VtZARERERDTZMVBARIddS09I2a4pd2JmnVd5va8rqAQRMvoCo5oxhg2ZQIFHXZzHEymEhCkK4tjC7OkFQNbkg7HSA7EcYmadT/fajTIHyplRQERERERHCQYKiOigeHtHD/70zFbs7hgqeGz2SMHacrX0IBZPYThrbOKuNrUXQLm3CO7idDaAs8gCu9WsvDcYVBshtmqmEeQ2GRT7FARCUYQjcc24xpqy4pxzAMDv088c8DOjgIiIiIiOEgwUENF71jM4gt8/uQVr3u3CLx9cXzBY0C4s4uv9LjjsFphN6kjE0Ig2UNCk058ASE8hKBV6BgwE030KhoZjSoNCAKivyG1K6M0akdg1oPZMcNjN8DhtOecAQEWJfgCBGQVEREREdLRgoICI3rM3t/UgmUqXC8QSKfz+iS1IplKGx4uL8upyJyRJgrvYquwLjcQ0x+9uUwMPDVmTCMQ+BQNjoxXbetWMBU+xFV6dRb9P2BcIx9AlNFesKi2GJEk55wDp3gZ6TQ7Zo4CIiIiIjhYMFBBRjoHgKFIpufCBY97a3qN53R8c1Uw1EEVjSQyG1BKBqtL0E3pPsbpwFzMKZFnWZhRk9RsoEfoUZDIKxP4EdRUu3esQMwZCIzFN8CJzTXpMkpRTfmC1mHSDEUREREREkxEDBUSk8eALO3HNna/hxvvX5c0KyOgeGNH0HMjoMggUiAtyi9mEsrGFvlFGQf/QqOb11P3MKKjzFw4UBIfHHygAchsalnuLDDMQiIiIiIgmGwYKiEgRjsTxwro2AMC+rhC2twQKntPUpt+PoHNAP1DQPajuryx1wDTWm8AtZBQEhYyCfV3qhAS/rwjOIjWgAGgnH2QyCsRsBrFRosiT9X1ioKCyUKDAqw0UTNOZqkBERERENFkxUEBEig1NfZrXLcIi3UhH37DufsOMgn79J/fuYm0pQIYYKJhalbsgL9MECqKQZRmd/eo1VRsFCoSMgngipfk5CmUUnDTbr2yfMKscyz84K+/xRERERESTieVIXwARTRzv7OzVvG7uHkegQFyUlxUrT/O7DDIKjFL8xdIDbUZBUNmeWp07vUCcejAYimIwFEUkmlT2GY05dDmskCRA1mnFUCijYE5DCX5y5WIkkik0VLpZdkBERERERxVmFBARACCeSOLdvQOaffs6CwcKxFGH4pP2roFhyFmrcFmWNf0MxECB+IQ/PJZRIMsymsWMgkqdQIFbzShIyTK2NQ8qr71OG4qzShUyTCYJbkfue2UeO+xWs+45ojq/C1OrPAwSEBEREdFRh4ECoqNYIpnKWawb6QmMIpFMZe2LYHg0bnAGMBpLoH+sLwAAnNioBgoi0SSCw9oxh01tQ2gXUvzF2n5x0Z6ZetA7NIrh0YSyP3s0IgDYbWY4i9TkqC371GBHtUE2QYZHZ1JBobIDIiIiIqKjHQMFREchWZbx3Bst+PpvXsV1d69FTyBS8JzeQf1j9uXpUyA2DbRZTJhS6daUEHRnfebz61qV7TlTfKgR+gdomxmmAwxiNkFNudMwO6BEyCrYImRFGPUn0PvOjEJlB0RERERERzsGCoiOQo+v2oNHXtqFaCyJ3sAo/vDUlpxsgWxGwQSjAAKgbWRYXeaESZI0pQBDQkZBIpnSNEv80Cn1ms9yO9UgQDgSRyqrKWFDnskCZUKfgpDQ36CmLH+gwMuMAiIiIiKiHAwUEB2FXt/SrXm9pyOI1Zs6855jFBAQSwuy7elQGw3W+tOLcnHBL5YedA9GkEylyyBMkoSF08s0n+V2qIt2WQaGI3F0D6jXVFfhMrwOcUSiSK9UQfOdOhkFVQXKFYiIiIiIjnYMFBAdZZKpFAZD0Zz9O9sCec/rHVIX5Xab2swvX6Bge4vaOLCx3gcA8IglBEKgoFPIPqgoccBi1v7147CbYTGrjQGHwjF0D6qlDTV5ygjEyQcZFrOEBp3mhyKPM7eUgRkFRERERHSsY6CA6CgzEIwipdPAMF8JAQD0CO/PGVv0A8DAkH6gYGg4pulRMGdK+hyxQWCm1wAATRmBXpNBSZJQ5nWo1xOIoFsYpVjjz5NR4M7NKGiocsNqyf9XXHYzQ3ex1TA7gYiIiIjoWMFAAdFRps9gYZ+voWEqJaNPyCiY01CibPcHc7MTAGCHkE1Q4rbD70sv8o0yCjr6C2cHVJaogYI9HUHNxIOa8nylB7kZBTNrvYbHK+dlBRg+e9YsmDjukIiIiIiOcQwUEB1lxAV/mfB0PDQSRySa0DsFg6EoEkk1C2HOlBLNe6lUbobCtuZB4XgfpLEFtjj1QGwsKJYeGDUZrCxRMw027e5Xtp1FFnhduf0EMkp0sgDGEyiYPcWHWXVeWC0mLP/gLCxdUFXwHCIiIiKio52l8CFENJn0BdSMghm1HoRGYogl0hMPegYjug3+dncMKdtel03T0C8lywiEo5qU/JQsayYYzJtaqp7vzM0oSMkyuoQygupy/T4AFUJGQVtvWD3e71ICEXpK3Xa4HFaEI+nAhM9l01yTEYvZhO9eehJkWc77+URERERExxIGCoiOMmLpgd/ngN/nQPvY0/zegH6g4K1tPcr2/KmlsFvNmoX3QFAbKNjbEVRGH5okCcfNLFfeEycJZHoUDAyNKsEKAKguNcgoKHXo7s/XyBBIL/i/cdEirHm3E7XlTpw6vwoO+/j/emOQgIiIiIhIxUAB0VGmXyg9yA4U6PUpiEQT2LRHTfNfPLcSQLpsIRMo6A+OYibUVP53mnqV7cZ6L1wOtdxAbBA4GksiFk9qghdel00zVUEklh6IphSYXgAAM+u8mFlXuNyAiIiIiIjyY48CogMgyzL6AhHd6QL5xBP6owsPpl5hUV7mLdKk8/foTD54a3sP4mNP+51FFsybmu5PIDYIHMgakbh+p1p2cEKjX/Oe2KMASGcViCMWy/NMFSjzFMFsyn2631DtMTyHiIiIiIgOLmYUEB2A//vHNqx5twuz63249nMnjKtT/mgsgZv++g5ausM4b2kDLvrAjILnpGQZb+/oxY6WQRTZLDhvaUPelPpINIGAEIgo9xYp0wgAbaNDIB3wePHtNuX14rmVsJjT8UOxEaK40O/sH9b0Gzhhllp2AKTLAJxFFmViQXA4jv6s4IURk0lCRYlDM3YRABqqGCggIiIiIjpcmFFAtJ/CkTjWvNsFANjRGsCe9uC4znt9SzdautMN+v6xthltPeECZwCvrG/HXU+8i/+8045/vt6Me57eCjlPFsPujiFk3i22W+D3OeBzqZkBQ8K4QgDY2RpAq3AdZ51Up2yLPQkGhBGJ7+xUyw6mVLpQ7s3tKyCWHwRHYugTAg1leTIKAKCx3qd5bbOaUFmqX5JAREREREQHHwMFRPtpb6c2MCBODMjn1U0dmtdPrN5b8JzNewY0rzfs6sO6Hb0GRwNNreq1zKzzwiRJulMIMtYLkwvmNpSgVmgaKD75FzMKxGkHJ87Slh1kaBoaDsfGnVEAACdkfabf54BJpxyBiIiIiIgODQYKiPbTng5toKCprXCgoL03jL2dIc2+9U29iMaSec/rHhzJ2ffEq3sMswp2tavXMmussZ/HpS7awyNxJFPq9IGWbvWajptRpvksvR4FqZSMZuGchVnnZJS4tef270dGwdyGEs3rVGr/+kAQEREREdF7w0AB0X7KDRQE8pYDAMDWfYM5+2Q5t2eAKJlK6TYf7OwfwW6dcodkKqW5tpm16UCBV3i6LwMIjcTHvl9WSiGA3MkC4oJ+eDSBSDSB/uAoEkn1ZzUaW1iqCRRENc0QC2UUWC0mnDqvUnn9oZPr8x5PREREREQHFwMFRPtBluWc0oPQSFzT3E9Pa69+PwJxQkG2vsAokmNP0yWoC38AWLWxI+f49t5hROPpDAWzScK0sUkBdptZM44wU37QHxzFSDSh7K+vdGk+z+O0aSYQDARH0S38nKUeO+xW/TGHYn+DfV1BTXChUEYBAFx85kyc1OjH+4+rwWkLqwoeT0REREREBw8DBUT7oX9oFOFIPGd/R1/+QIFR48K+gHFGgRh8KPMW4ayTapXX65ty+xSI2QG15U7YhEW8mFWQaWgoHl/mKYKzSDvW0CRJmvKD/mAUncI1VZYYNxgUz2vrHVa2nUWWvFMbMkrcdnztwoX4wkfnwGrRD0YQEREREdGhMWnGIz777LP461//ii1btkCWZdTX1+Pcc8/FihUrUFSkfUIZiUTw5z//Gf/617/Q3NyMVCqF2tpanH322fiv//ov+Hy+nM9PJBJYuXIlHn74Yezbtw8WiwULFy7El770JSxZsuQw/ZR0uMUTKaza2IHKUgcWTNOvtxcZZQAEwlHd/UC6xr69T10sN1S6lTr/vjwZBWKgoKq0GI11PuX18GgC8UQKVosa6xP7DWSXEXhcNvSMBSWCSqBAPF6bTZBR5ilCbyB9jdkZBVV5JhGUuvWzBvQmJBARERER0cQyKTIKbr/9dlx99dV45513sGDBAixduhQDAwO47bbb8KlPfQpDQ2oDt0AggE9/+tP4zW9+g/b2dpxwwglYvHgxBgYGcM899+DCCy9EV1eX5vNlWcZ3vvMd/PCHP0RbWxuWLVuGxsZGvPbaa7j88suxcuXKw/0j02Hyp39sxV+f34lfP7IRO1py+whk6zdY2A+EjBf83YMjiCfSDQQlCThuphqQ6B1nRkFVaTGcDu0T/5FRbWZDi5C1kF1GoJdRID7pr6/QDxSIJQT9WYGCfCMLxYwCUa1fv6cBERERERFNHBM+o2DdunW444474PF48MADD2DOnDkAgJGREXzjG9/Aq6++ittuuw033HADAOCXv/wldu7cicWLF+M3v/kNSktLAQDBYBD/8z//g1dffRU/+MEPcM899yjfsXLlSjzzzDOYO3cu7r33XiXjYO3atbjqqqvw4x//GMuWLUN1dfXh/eHpkGrpDuHNbT0A0o0Fn1nbjNlTSvKeY9R8cDBknFEgLsgrS4o1DQAzT+v1iL0QKkuLYbOYYDGbkEimgw7hSBxeV3pBnpJltPaoGQIN2RkFOiMSxYkKhk0JswIFXQPqz19Vapwd4HJYYbOYEEukNPvr/PoBCSIiIiIimjgmfEbB3//+dwDAF7/4RSVIAADFxcX4xje+AQB4+eWXAQCjo6N45plnAAA33XSTEiQAAI/Hg5tvvhmSJOHVV1/F4KD69Pjuu+8GAFx//fWasoSlS5fi8ssvRzQaxQMPPHBIfj46OMKROG57dCN++dD6vIt20T9fb9a83rpvoOC54pg/Z5EaZxsMGp+3r0td8NdXuOD3qQvsvqGI7sSEvZ1BTQ+B2fU+SJIEp0P9zuFRtRFhXyCCSFQdtZidIeB1ajMKUrKsmahg1G9AzAzoHohophfkKz2QJAklOk0LmVFARERERDTxTfhAwY9//GP885//xGc/+9mc95LJsQ7v5nSzs/7+fsyfPx8nnngiamtrc44vKyuD1+uFLMvo6Uk/Sd61axdaWlpQVlaGk046Keecc845BwDw4osvHrSfiQ6+/7zdhk27+7GteRCPr9pd8PhUSsaGXX2afbIMvLG1O+95YunBLKFnwGCeHgVb9g4o29NrPCgXxgOOxpKaBX/GS+vble2ZtV7UjS38XUL5gdhUUcxaqPA5choGZmcUBEJRpRwCACpK9LMDxAkFezuDyIQ00o0O808vEEckZjCjgIiIiIho4pvwgQKLxYIZM2bA4/Fo9nd2duLmm28GAFx44YUAgNraWjz44IN46KGHdD+rubkZgUAAJpMJlZXpOe07d+4EAMyaNQuSJOWck9nf0tKCSMS4npyOrCdW71W212zuynNkWufACGLxVM5+8em/nj5NoEAdVzgYiupmBgyFo5rMgIXTy+ByWDXjCrPLGWRZxvqd6lSDM09Qg17iZIJhIVDQ2a8GCvTKCLIzCrqFbAKP02Y4iUBvsQ+kMw0s5vx/fWSPQSyymeFz2QyOJiIiIiKiiWLCBwqy3XTTTVi+fDnOPvtsbN68GVdccQW+/OUvj+vcW265BQCwbNkypcQgk1mQCRxks9vt8Hg8SKVS6Ovr0z2GDq7dHUN4aX07ItHcJ+3jNTyaO8JQ1NIV0t3fbLAfSGchiKUJM2rVQEE8kdLNDHhXyCYo9dhRXVYMSZI0C/DscofBUFTzWYuE5odiuYN4TFe/2m+guiy3JCDTywAAAqGopj9BpUE2Qfqa9bMGxPIJI8sWVGleT6lw6QbjiIiIiIhoYpnwzQyzrVy5EsFg+qmvzWZDb28v+vr6UFFRkfe83//+9/j3v/+NoqIifPvb31b2Dw+nn8Rmj1gU2e12zbEHi9Vqht/vLnzgMSDz36Grfxg3//UdJJIymtqH8P0VhUdTjsZyF+h94Tim1pfqHJ3WE1T7E8ydWopt+9IL+u7BCJzuIhQXWXPO6R2MIJlSswZOml8Nm9WMWHysN4Al989ze+t2ZfuUeVWoqEhnxlSWOtE5trhPQNKct7dHvc/KfQ7Nz1Eu9BJISep5fUKPhMappTnXYbKp/6uPRBOa4xuqvXnvQ2eRJScIUl/lKXjv+v1uxCHhd49uQCyRwnnvm/Ge73f+/0KF8B6h8eB9QuPB+4QK4T1C4zFZ75NJl1Hw1FNPYePGjXj00Udx4okn4umnn8by5csxMjJieM5vf/tb/PrXv4bJZMLPfvYzzJ49W3kv09+ATzonhje2dCGRTC/GX3+3C209xk/4M9qE1P6MzMLfyK62gLJ92nE1KBae1O/t0C8/6BGewnucNhTZLSgT+g1kj04Mj8Tw5la1DGLxPPUJe6lwXl/WiMS9Heq4z+k1Xs17bmHMYWgkPb1AlmXNf6e6ity/jHwuO0zCLb55d7+yXW0w8SDDr9PosEona0HPWSfX474bzsE93/sgzjq5flznEBERERHRkTXpMgoyIwoXLVqEe+65BxdddBF27tyJRx55BF/4whc0x8ZiMVx//fV48sknYbFY8POf/xznnXee5hinM71IGh01HlMXjaafvjochdOt90c8nkQgYBzgOBZkImy9vemF7u6WQc37K1/Yic99qDHvZ7zb1JO7b1cvenvrdI9PyTJ2C4GCcpcNdX4Xdram923c0Y0Kd24t/cbtaqNDv68Ivb0heIut6Bzbt7d1EA3l6gL65Q3tSsNAd7EV9WUO5ed0WNUYXUd3SNkPANv3qov4irHvyZBkta9C/+AIentDGAprSxXsJmjOyfC67EqZQ2u3+r7LbtY9PsNTnJtd4bCY8p6TzQz9axqv7PuEKBvvERoP3ic0HrxPqBDeIzQeE/E+8fmKYbWaCx+ISZhRILLZbPjoRz8KANi6davmvf7+flx22WV48skn4XK5cPfdd+P888/P+YxMb4Le3t6c94B0kCAYDEKSJPj9/oP8E1C21h5tdsDaLV26TQJFHX25JSGd/cYBmP6hUYzGtKMEGyrVp/CtOhkKALBTCC7MqvUBSD+pzxgajmmOf1OYoLBkXqWm+V+J2KMga2JCi/DfYErWmEO9qQfiz+optmqOEYnXKspuOphNr0/BeHoUEBERERHR5DThAwW//e1vcfXVV6OrS7+Tvc2WfvKbSKhPVFtaWnDxxRdj/fr1qKurw9/+9jecfvrpuudnyhCampp0329qaoIsy6ivr0dx8fjSrenApGQZrb3aRfrwaAKhSP7GhOJowIzBUNSwGaIYWCjzFMFht6CyVF346o06lGUZTWMZB4A68cCTNU3A6LpOnKUNMpW49JsZjozG0StMJKjPChRoph6MZRGIwZWqMuMyAqMJBiUG+/OdV+7LH1wgIiIiIqLJa8IHCtasWYNnn30W//jHP3Tff+WVVwAACxcuBAB0d3fjsssuQ3t7OxYtWoRHHnkEs2bNMvz8hoYGTJ06FT09PdiwYUPO+8899xwA4IwzznhvPwgV1BeIICo86c/oDRiPpZRlGc0GIw3Fzv6iDmGUYK0/vbD26NT+i3oGIwiOqAGLWfU+AIBXGPc3JAQYwpG48sQfyO0D4BMW3wHhvB2tAWTyJ1wOK/xZEwmcDjFQkP78d4RRitOrtWNEjb4zwyRJmtGJeiqyrqHYboHbIGuBiIiIiIgmvwkfKLjkkksAAHfccQc2bdqk7I/H47jlllvw5ptvoqysDBdddBEA4Nprr0VnZydmz56Ne++9F2VlZbqfK7rssssAAD/4wQ80IxDXrl2L+++/H1arFVdcccXB/LFIR3bZQUa+QMFgKKpZwJd61MWwUflBh/Ckv2bsCbxbqMMPjeRmMGzYpd4XNeVOJb1fXGQHhYyC7gH1ux12c06dv/gUPxJNKtkP25rVHg1zpvhgymqymV16MBiKKr0VAODkOcbTP/QyB3xuG0ym/I08T5hVjhk16QCE1WLCJ943jc0/iYiIiIiOYhO+meH555+PdevW4eGHH8ZnPvMZnHDCCfB4PNi2bRu6urrg8/lw1113wePxYM2aNXjjjTcAAG63GzfccIPh537zm99EfX26C/vy5cuxatUqvPzyyzjnnHOwZMkShEIhrFu3DrIs46abblKaKNKh02GwsO8NGDeabO5Sm4OUeeyYUevFm8F0c0PDQIGQUVA91nxQnCYQHI5BlmVlMRyJJvCPteo4xQXT1HGFXqd+j4IuIVBQWVKcs7D2FNtgNknKuMVAOAqH3YLtzQHlmLkNJTnX7hSmM8TiKbyxtVvJQCjzFGFatfH4Fb1AQYlB3wKR1WLG9y87GcOjcVjNJtjG2QCFiIiIiIgmpwkfKACAn/zkJzj11FPx0EMPYcuWLYjFYqipqcHll1+OK6+8UmlI+NJLLynnrFu3Lu9nXn755UqgwGQy4Y477sBf/vIXPP7441i9ejVcLheWLVuGq666CosXLz50PxwpsscEZuTLKNgnBAoaqjyoFmr0u/pzexfIsoyOPnURXzNWEiD2GkimZESiCRSP9QN4eUO7UkZgs5rwkSVTlGO9WT0KMgEGseyhqjS3t4XJJMHrsmEgmC47GAxF4XRY0Sb0aJijEyjIblS4abea6XBCY3neJ/16QYFC/QlEYn8EIiIiIiI6ek2KQAEAnHvuuTj33HPzHnP99dfj+uuvP6DPt1qtWLFiBVasWHFA59N71zekZg7U+p1oHysRMAogAEBztxgocKNSqKfvHMjNKOgPjiIaV/sgZEoPiossMEkSUmMTFkIjcSVQ0NQ6pBx/1gl1mukBHqFHQTyRQiSaRHGRBV0D6jVX6gQKgPTCPRMoCISjyihFIB2A0Asw2KxmlHrU87a3BJT3qvM0MgSAEo9eoIBNCYmIiIiISGvC9yigyWvjrj787vHN4xpxCGgzB+Y1lOruF8myjD0daiPDqVVuVJaoi+v+odySBbEHQLk3PfEASDf1cwl9BIJCQ0Oxd8Kseq/m81wOq6aPwNBwegEv9igQJyqIPJr+BnG0CEGPqVVuw+yAer9Ld3+hMYd+ryOnceH+ZBQQEREREdGxgYECOiRGRuO468l38fbOXtzz9Fbc++z2vMGCZCqlPCUHgLlT1bT7gWAUiWQq55zeQEQzWWBatUfT2X80lswZkbhxV7+yvXCGttGl2HAwOBxXfo7+oBpwyB5XaJIkeJzieTGkZFkbKCjRzyjQli1E0dKtBiSmVBr3GqivNAoU5F/0m0wSTslqdlha4BwiIiIiIjr2MFBAh8SGXX2IxdXF/aubOtHel9szIGMgGFXS/gFgdr0PmefpMoCBUDTnnN1CNkFVaTFcDivcxVaYhS7+4ujBeCKFLXsHlNfHzSjXfJ7Y0DAUSWcUtAkTEhx2i+5T++yGhgNDo4gJZQTVZfqBAk9WfwOxjCJfoGBKhf57pQUyCgBgybxKzetCoxGJiIiIiOjYw0ABHRLrtvfm7OsdNO41IPYh8LlscNgtcDrEkYWxnHN2t6u9AzLj+0xSuklgRkAIMOxsCyj9CWxWE+Y2+DSfpxmRODbBQCw7qPc7dcsBxO8bCsc00xtK3HYU2fRbgXhd2lGOYo+GBoOsASA3qwEAiu0WpYwin+k1HuV8h92SNyBBRERERETHpknTzJAmj9FYAu8KT+4z9LICMnqFRXK5L13T7y62KqUFoZF4zjliRsGMWrV3gE9oEjgoZBTsEQILs+tLYLVox/x5xBGJY98nBgrqdBbogDYzIDAc1ZRY1BhkE2R/nzjm0VlkQZnXODvAX+KA3WrWNGUcTzYBAEiShG9+ahHe3NaDeVNLxhVcICIiIiKiYwtXCZRXXyCCf77ejObuEGbV+fDps2Zqmvfp2dMR1O0pMBDKbS6YITYs9I8tkt0OKzrH9mVnFIzGEmgVavqnj2UUANoxgIGwep44SnFade6TdLew4M98nyajwCBQIE5BCISiGI6ofRHyTSIQMxFEUyqNGxkC6ayJWfVevLtHDcaU5wksZCv1FGlGPBIREREREYkYKKC8bn98s7JY3tsZQn2FC6ctrM57jtiUTzSYJ6OgqTWgbGfGAoo9A8JZGQW72oeUngYOuwV1wiQAsaGh+J1ioGBqtRpYyBCbGYZG4kilZLT3Fc4oEJsI9g+NQmzZWF2eJ1Bg0B9gSp6yg4xl86s0gQJxYgMREREREdF7wR4FZCgWT2qeqAPAs2+0aJoO6hHPEVPbxakGouBwDE1CWcCC6elpBO6shbtoR0tA2W6s88IkNDD06fQoGApHNUGDqVW5GQViU8L+4Ch6AhGlIaMEoK7cIFAgPM3vD0bRKfQoyFt6YBgoKNw34IRGv+a1w6APAhERERER0f5ioIAMifX9GR19w5rJAXpae9Qn98cJIwgHDUoP1m3rRib24HXZ0DC2iHcV55YCZIiBgtlTSjTvlbjF0oP0zyBmE/hcNk25QEaVsKjvDUSwV+iBUFHigN1mzjkHgGYSQn9wVDOysSpP6YHdakaRzmc2jCNQYLea8eFT6pXXSxdU5jmaiIiIiIho/PgYkgwFDEoFmtqGsHB6me578URK80R90cwyvL61G0C6DCAlyzk9Dt7Y0qlsHzejXHlfk1EgLL6j8ST2dqqL+NlTfJrPE4MAgzqBgqlVuWUHAOD3FcFilpBIypBlYN2OHuU9o/4EgHEjQWeRRVPOoMfrtGE0pvZnsFlNSulFIZ86YwamVLpQ5iky/JmIiIiIiIj2FzMKyJDRlIKugRHd/UA64yCZSqcHWMwS5k8tVd5LJOWcXgMjo3GsGwskAMAJs8qVbW3pgZpRsLt9SPmOIps5p6ZfzCgYCseQkmXsEwILU3UaGQKA2WRCpbBIX9/Up2wb9ScA0k/3XY7cgEB1mf44RVF2+UG936Upo8jHYjZh2YLqnIwKIiIiIiKi94KBAjJklFHQnSdQsHZLl7JdU+6Ey2HVpOxnTz54c2s3Yol0H4BiuwXzp6mBBbem9EANMIhlBzPrvDCbtLexmFGQTKWDE+PJKACAGoNSgXp//gaDeuMMq/P0J8jILoForPcVPIeIiIiIiOhQYqCADIkZBTNq1cV198CIbkPDnsERvPh2m/J68dxKSJKEUnEKQVZDw1fXtyvbJ832w2JWb0m3Q7/0YIcwIWGOztP0IpsZNov6Oc3dIQwNqxkJeo0MM4wW9zV5phcA2j4F6mflPwcATltYDYtZgtkk4Yzja/CxZVMLnkNERERERHQosUcBGRIzCuZMKcHu9nT6fiyRwmAwmvMU/dVNnUpJQJnHjg+eVAcAKHXblb4FYvAhmUph065e5fUpcys0nydmFERjScQTScgysKdDnZAwW+cJvCRJ8Dht6BtKZy9s2tWvvFfqsRtOGwD0AwImSdLNGBCVenKbI44no2DRjDLc+rXTYLWYUMTJBURERERENAEwo4AMiYv6mjInvMLYQb0+BeKT/rNOrIPNmi45KBGetoulB209wxiNJQEAZpOEWXU+zedl1/2HxkoIEsl0MMJuNSsTErJ5hWDAxt1qr4FCTf/0AgXlviJNpoOecq8jZ191gSyEDHexjUECIiIiIiKaMBgoIEMBYTxiiduOaqHRX3agIBZPasYJig32jEoPdrWrmQFTKt2wW7WjAq0WExx2dV9oJI6OvmHldX2ly3ABL2YNZDILgPxlB0A6IJI9BrGypHBmwIlCE8aMcoNpCERERERERBMZAwWkK5lK5QQKxLF9Xf3aQMGejqBSdpB+0u/SnJshZimIgYKZtV7d63A71AV/cCSmGb1Ykye13+vKLQUAckcpZjOZJEyv1mYdVJbmZgtkK/c58OVPzFdeHz+zfNzTC4iIiIiIiCYS5juTruHRBMR+hT63XZOW39wT0hwvlh1kTyIoFZ6sDwqlB7va1HNm1ekHCnwuG3oCkbFzo+gcUDMKqkqNU/s9xbnjCu1WM6ZV5y89ANKNG7c1Dyqvx5NRAKSbN5a47djVNoSlC6rGdQ4REREREdFEw0AB6XI7rKjzO9HWO4xFM8pgt5o19f0t3SGkUrLy1Hy30GAwe8SfmFEwGIoiJcsIhKLoD4pTFQwCBcK5gVBUk8mQr1mgXkbBrDpvwV4DADCjRnst48koUL/Dl9NrgYiIiIiIaDJhoIB0SZKE7156Etp7hzGtJl3XX1/pgiQBsgzE4il0DoygttwJWZbR3KVmGEyr1vYBKHWrGQWJpIzwSFxTdlBRWqwJJojE/d2DEfQL/QbyBgp0JhvMbcgdpagnO2gx3owCIiIiIiKiowF7FJAhh92iKSOwW82oKRPKD7rSzQsHQ1GERuLK/oZKd9bnmDUNAgdCo5pAwdyGUsNrKBEyA7a3DCJTDWExm3QnDWToBQrmTzP+HpHLYcVHlkyB2STh9EXV8PvGn1FAREREREQ02TGjgPZLQ5Ub7WOTB5q7wli2AGjuVrMJyjx2uIu1i3RJklDqtiuNCAeDUexqEwIFeRbwvqyyhYyqUkfeZoHZgYJSjx31FS6Do3N9+syZ+OT7psNqYSyNiIiIiIiOLVwF0X5pEMYLto41NBTLDqZU6o8fFEckdg2MoKU7rLyeOzVPRoFBSUJVmXEjQ0A7HhEAGut8kKT9m0LAIAERERERER2LuBKi/SKm4Wee8IuBgqlVBoECYfLB+l19SI2NVLDbzGjIM4mgxGDMYXVp/r4BNqsZXpcaLPjA8TV5jyciIiIiIqI0Bgpov4iZAYPhKGRZRke/OrKwvkI/UFAnpP2LZQdVpcUw5ykh8BlkFORrZJix/OxZqC4rxnlLGzB7yvgaGRIRERERER3r2KOA9ou4cI/FUwhF4ugfUnsHGI0SzG5wmOEvMFHAYjbBXWzVNEsEgOoCpQcAsHhuJRbPrSx4HBEREREREamYUUD7xe2wwmJWMwB2tw0pZQQSYDiJwKiRoL+k8EQBvfIDo4AEERERERERvTcMFNB+kSQJPmHhvqM1oGyXeuyGDQAddgsqdYICFQUyCoDcYEKJ244iG5NhiIiIiIiIDgUGCmi/iX0KxECB2OhQj95EhIpxZBRkNyK0mnnbEhERERERHSpccdF+E/sUiBMPCi36p1brBQoKZxQsmFaGOVN8yusTG/3juEoiIiIiIiI6EAwU0H4rMZhEUGjRv2h6Wc6+8fQoAIAvfnw+5kzxobHOiw+eXDeuc4iIiIiIiGj/sdCb9luJu0h3f0WB0oOa8txJBUaflXucHd/+3InjOpaIiIiIiIgOHDMKaL8ZZxTkDxRIkoRFM7RZBSaTZHA0ERERERERHQkMFNB+8/tyswAcdgtq/bkZA9k+e/Ys2MYmI5x5Yu1BvzYiIiIiIiJ6b1h6QPttSqUbJW47BkNRZd+8hhKYTYXjTlWlxbj+8pPR1T+C42aWH8rLJCIiIiIiogPAjALabyZJwilzKjT75k8vHff5dX4XTp5TAauFtx8REREREdFEw5UaHZAl8yo1rxdMHX+ggIiI8fFCggAAInlJREFUiIiIiCYuBgrogEytcuPksayC0xdWo7zAxAMiIiIiIiKaHNijgA6IJEn46gULMDwah7PIeqQvh4iIiIiIiA4SZhTQe8IgARERERER0dGFgQIiIiIiIiIiUjBQQEREREREREQKBgqIiIiIiIiISMFAAREREREREREpGCggIiIiIiIiIgUDBURERERERESkYKCAiIiIiIiIiBQMFBARERERERGRgoECIiIiIiIiIlIwUEBERERERERECgYKiIiIiIiIiEjBQAERERERERERKRgoICIiIiIiIiIFAwVEREREREREpGCggIiIiIiIiIgUDBQQERERERERkYKBAiIiIiIiIiJSMFBARERERERERAoGCoiIiIiIiIhIwUABERERERERESkYKCAiIiIiIiIiBQMFRERERERERKRgoICIiIiIiIiIFAwUEBEREREREZGCgQIiIiIiIiIiUjBQQEREREREREQKBgqIiIiIiIiISCHJsiwf6Ys4VsmyjEQidaQv44iyWs0AgHg8eYSvhCYy3idUCO8RGg/eJzQevE+oEN4jNB4T8T6xWEyQJGlcxzJQQEREREREREQKlh4QERERERERkYKBAiIiIiIiIiJSMFBARERERERERAoGCoiIiIiIiIhIwUABERERERERESkYKCAiIiIiIiIiBQMFRERERERERKRgoICIiIiIiIiIFAwUEBEREREREZGCgQIiIiIiIiIiUjBQQEREREREREQKBgqIiIiIiIiISMFAAREREREREREpGCggIiIiIiIiIgUDBURERERERESkYKCAiIiIiIiIiBQMFBARERERERGRwnKkL4Amp3379uGCCy7AhRdeiB/+8Ic578diMdx33314+umn0dzcjOLiYsybNw+f//znccYZZxh+7n/+8x/85S9/wbvvvouRkRHU1NTgnHPOwVVXXQWXy5Vz/NDQEP74xz/i+eefR0dHBzweD04//XR87WtfQ319/cH8kekAHMz75PHHH8d3v/vdcX3v/fffjyVLliiveZ9MXIfi75KhoSH8/ve/xwsvvIDOzk7Y7XbMmzcPl156Kc455xzDc3iPTFyH4j7p6enB3XffjZdeegk9PT3w+/045ZRTcOWVV2L27Nm65/A+mXiefPJJPPbYY9i+fTsikQjKyspw6qmn4ktf+hJmzJiRc/xrr72Ge+65B9u3b8fo6CimT5+Oz372s/jUpz4FSZJyjk8kEli5ciUefvhh7Nu3DxaLBQsXLsSXvvQlzb8zIt4nE8+hvk+yff/738djjz2GtWvXorS0VPcY3icTy6G+R9rb23HPPfdg9erV6OrqgtVqxcyZM3HBBRfgs5/9LMxmc845R/oekWRZlg/5t9BRpa+vD5dddhl2796NSy65JOeXtnA4jBUrVmDTpk2wWq1YuHAhnE4nNm7ciGAwiM9//vP4/ve/n/M/0U033YQ///nPsFgsOOWUU2Cz2bB+/XoEg0EsWrQIf/nLX2C325XjA4EALrnkEuzatQtTpkzB3LlzsXfvXuzcuRMulwt//etfMWfOnMPy34RyHez7ZN26dfjb3/5m+H27d+/G1q1b4fF48OSTT6KmpgYA75OJ7FD8XdLf34/ly5ejubkZfr8fixYtwtDQEN555x2kUin813/9F6699lrN9/AemdgOxX2yY8cOrFixAv39/XC73TjuuOMQiUSwadMmAMDPf/5zfPzjH9d8D++TiUWWZVxzzTV45plnlMV7aWkptm/fjvb2dhQVFeF3v/sdTj/9dOWchx56CD/60Y9gtVqxZMkSWK1WvP7664hEIrjwwgvx85//3PA7vF4vFi9ejEAggLfffhuyLOOnP/0pLrroIs05vE8mlsNxn2R74IEHcOONNwKAYaCA98nEcTjukU2bNmHFihUIh8OoqqrC3LlzEQ6HsWHDBsTjcbz//e/HnXfeCavVqpwzIe4RmWg/bN26Vf7Qhz4kNzY2yo2NjfKPf/zjnGO+973vyY2NjfKZZ54pb9u2Tdnf398vf+5zn5MbGxvlRx55RHPOs88+Kzc2NsqnnXaavH37ds05n/rUp+TGxkb5zjvv1Jxz7bXXyo2NjfJ1110nJxIJZf9dd90lNzY2yh/72MfkZDJ5sH502g+H6j4x0tvbK59++uny7Nmz5VWrVmne430yMR2qe+T666+XGxsb5a9+9atyJBJR9r/99tvyokWL5MbGRnnTpk2ac3iPTFyH4j5JJBLyOeecIzc2Nspf+MIX5P7+fs33nXbaafL8+fPlXbt2ab6H98nE8sQTTyi/N4h/7olEQv7Vr34lNzY2ykuXLpXD4bAsy7K8Z88eee7cufKJJ54ob9myRTm+vb1d/uAHPyg3NjbKzz77rOY7Hn30UbmxsVH+xCc+IQ8ODir7X3vtNXnhwoXywoUL5Y6ODs05vE8mlsNxn2TE43H5l7/8pfL3VWNjo+bvFxHvk4njUN8jyWRS/vCHPyw3NjbKN954oxyLxZT3du/eLZ955plyY2Oj/Ic//EFzXRPhHmGggMYlEAjIv/jFL+QFCxbIjY2N8llnnaX7S1s4HJbnz58vNzY2yq+99lrO57S1tcnz5s2Tly1bprnpzzvvPLmxsVFevXp1zjlr166VTzvtNPn//b//p+xrbW2V586dK5988slyKBTKOefTn/603NjYKL/88svv5cem/XSo7xM9qVRKvuyyy+TGxkb5pptu0rzH+2TiOdT3yAc+8AG5sbFRfvfdd3PO+Z//+R+5sbFRvueee5R9vEcmpkN5n6xevVpubGyUTznlFN1f4p966im5sbFR/vrXv67s430y8Vx66aVyY2Oj/Pjjj+e8l0qllN8r/vWvf8myrAaUfve73+Ucv2rVKrmxsVG+6KKLNPszv/S/9dZbOefccsstcmNjo3zzzTcr+3ifTDyH4z6RZVles2aNfMEFF8iNjY3y2WefnTdQwPtkYjnU98jrr78uNzY2yh/60IfkeDyec84///lPZeGfMVHuETYzpHG5//778cc//hGlpaW46667cMEFF+get2fPHsTjcbhcLixdujTn/draWkyZMgV9fX3YsmULAKCpqQlNTU2YN28eTjvttJxzTj31VKxevRq33HKLsu+VV15BMpnEsmXLdHsXZOqQX3zxxQP5cekAHcr7xMgTTzyB119/HQ0NDbj66qs17/E+mXgO9T1iMqX/Wevq6so5p6+vDwBQUlKi7OM9MjEdyvtk+/btAIATTzxRNyU48zmvvvoqUqkUAN4nE5HH48GMGTNw8skn57wnSRKmTZsGAOju7gYAvPTSSwCAD3/4wznHL1u2DG63G5s3b1aO37VrF1paWlBWVoaTTjop5xy9P3PeJxPPob5PMlasWIFt27bhk5/8JFauXJn3mnifTCyH+h4Jh8NYtGgRzjjjDFgsue0Bp0+fDiDdNydjotwjbGZI41JVVYXvfOc7+NznPoeioiLDxVvmlyqn02n4WZn/SXbt2oVFixZh8+bNAIDjjz8eQLoW/ZVXXsHAwABqa2tx3nnnoaGhQfMZO3fuBAA0NjbqfsfMmTMBqL8Q0uFxKO8TPaFQSAkgffe739X0sAB4n0xEh/oe+cAHPoAHH3wQP/nJT2CxWHDyyScjHA7jz3/+M15//XVMmTIFH/nIR5TP4D0yMR3K+ySZTAKA7i9fAJSGUiMjI+jo6EBdXR3vkwnod7/7neF7yWRSuWeqq6vR19eH/v5+WK1W5ZdykdlsxvTp07Fx40bs2LEDlZWVyp/5rFmzdBuTZfa3tLQgEonA4XDwPpmADvV9kvHhD38YV111FRYsWFDwmnifTCyH+h45++yzcfbZZxt+R6Y3TlVVlbJvotwjDBTQuFx88cXjOq6hoQEmkwnd3d1obm7OWeD39/dj7969yjYANDc3A0g/5fvWt76Ff/7zn5pz7rzzTnz729/GZZddpuzLRN3Ev6RFFRUVmuPo8DiU94me++67D319fTjuuONw5pln5rzP+2TiOdT3yDXXXIPOzk689NJL+NKXvqQ552Mf+xi+973vaRaVvEcmpkN5n2R+uVu/fj1isRhsNpvmnLffflvZHhgYQF1dHe+TSebBBx9Ee3s7fD4fli5dqvyeUV5ermQdZcv+Myz0Z2632+HxeDA0NIS+vj7U19fzPplkDsZ9knH77beP+3t5n0weB/Me0TMyMoI777wTAHDuuecq+yfKPcLSAzqofD6fEjW75pprNKlZoVAI3/3udxGPxwGkx1ll9gPpLrEvv/wyfvjDH2LNmjVYtWoVrrnmGgDAT3/6Uzz//PPKZ42MjAAAHA6H7nUUFRVpjqOJ5UDuk2yjo6P461//CgD48pe/rHsM75PJ60DvEafTiY9//OOoqalBZWUlzjzzTBx33HGwWCz4z3/+k5MSyntkcjuQ++R973sfKioq0NbWhh//+McYHR1VztmzZ4+mW3XmHN4nk8fatWvxi1/8AkD6nnA6ncqfS+bPSU8mIy1z7PDw8LjPyRzL+2TyOFj3yYHgfTI5HOp7JBaL4Vvf+hY6OjowdepUzQPRiXKPMKOADrobbrgBO3fuxKZNm/CRj3wEixYtgs1mU0ZXfeQjH8Fzzz2njACJRqMAgGAwiFtvvRUf+9jHlM/64he/CFmWceutt+K2227Dhz70IQBqamihWbYyp39OWPt7n2R74oknMDAwgMbGRpx11lm6x/A+mdwO5B7JzK6+/PLLce211yrvbd26FV/72tdw6623oqioSPkHmffI5Le/94ndbsett96Kq666Co899hheeeUVLFiwAOFwGBs3bsSiRYvgdruxbds2pWyB98nk8NJLL+Hqq69GLBbD8uXLlcyUzJO/Qn9+gPpnON4/cxHvk8nhYN4nB4L3ycR3qO+RkZERfPOb38SqVavg8/lw5513aoICE+UeYUYBHXR+vx+PPvoorrjiCpSUlODtt99GU1MTPvaxj+Gpp56Cz+cDkG4eAqjRsrKyMk2QIOOSSy4BkK4vzaTYZFKHI5GI7jVknhAZReLoyNvf+yTb008/DQCGTc4A3ieT3f7eI2vXrsVjjz2GefPm4brrrtMEEObNm4ef/vSnANLlTJk6dd4jk9+B/F2yePFiPPbYY/jYxz6GRCKBNWvWIBAI4Jvf/CbuvfdeJYCdOYf3ycT3wAMP4Gtf+xpGR0dxySWX4IYbblDey/z5idkj2TJ/5sXFxft9TubPnffJxHew75MDwftkYjvU90hXVxcuvfRSrFq1Cn6/H/fddx9mzJihOWai3CPMKKBDwuv14jvf+Q6+853v5Ly3e/duAOlu1EA6QAAAdXV1up/ldDpRWlqKgYEBDA4OoqKiQqnZ6e3t1T0nE1DI1PDQxLQ/94mot7cX77zzDiRJwnnnnWf4+bxPJr/9uUfWrl0LADjttNN0awdPPfVUFBUVYXBwEB0dHaivr+c9cpQ4kL9LZsyYgVtvvTXn+FgshtbWVphMJlRXVwPg3yUTWSKRwE9+8hM8/PDDkCQJ3/rWt3LK0TJ/fn19fZBlWfcpXfafYaE/82g0imAwCEmS4Pf7x3UO75Mj51DdJweC98nEdDjukU2bNuGrX/0qent7MWPGDNx9992or6/POW6i3CPMKKCDbsuWLXjppZeUJ3aiTGqnyWTC/PnzAQCzZ88GgJxRMxnxeBxDQ0MA1KBC5pympibdc3bt2qU5jiae/b1PRKtWrUIqlcLxxx+v6RKbjffJ5La/90ggEACgpuxlkyRJCSBk6tZ5j0x++3ufRKNRvP3223jrrbd0P++tt95CPB7HrFmzlKc1vE8mptHRUVx11VV4+OGH4XA4cNttt+n2rPH5fKisrEQsFlOakYmSyST27NkDQP0zLPRn3tTUBFmWUV9frzw55H0yMR3K++RA8D6ZeA7HPfLSSy/h85//PHp7e3Haaafhb3/7m26QQDz3SN8jDBTQQXfjjTfiy1/+MjZs2JDz3mOPPYZYLIZly5Yp86uXLFmC4uJidHV14c0338w5Z/Xq1Ugmk5gxYwbKy8sBAO9///thMpmwevVq3UYe//rXvwBAtxM+TQz7e5+INm7cCAA45ZRT8n4H75PJbX/vkUzq3ssvv6xbt/f2229jZGQELpdL+ceZ98jkt7/3STKZxOWXX44vfelLus1SM01SxVI43icTTzKZxNe+9jWsXr0aZWVluP/++zWjT7N94AMfAAD8+9//znlvzZo1CIVCmDNnjhJ8bmhowNSpU9HT06N7bz333HMAgDPOOEPZx/tk4jnU98mB4H0ysRyOe2Tt2rX4+te/jtHRUVx88cX4wx/+YFhaC0yce4SBAjroPvzhDwMAbr31Vs3N/eabb+I3v/kNJEnC1Vdfrex3Op343Oc+ByDdiKylpUV5b+/evUpd8YoVK5T9lZWV+NCHPoRAIIAbbrhB88ve3XffjQ0bNmDmzJl555bSkbW/94lo8+bNAIDjjz8+73fwPpnc9vce+fjHPw6Px4Pt27fjF7/4heYJ8+7du/H9738fAHDppZcq/Qt4j0x++3ufFBcX4/TTT8fIyAh+9atfaYJK//d//4cXX3wR5eXluPTSS5X9vE8mnrvuugurV69GcXEx7rvvPixatCjv8ZdccgnMZrPy55XR0dGB//3f/wUAXHXVVZpzMk1Pf/CDH6Cvr0/Zv3btWtx///2wWq244oorlP28Tyaew3Gf7C/eJxPLob5HBgYG8P/+3/9DPB7HRRddhBtvvFFplGtkotwjksyWmnQAbr/9dtxxxx245JJL8MMf/lDzXiKRwOc//3m888478Pv9OO644zAwMID169cDAH7yk5/g05/+tOacWCyGr33ta1i1ahWsViuWLFmCZDKJ9evXY3R0FB//+Mdxyy23aM7p6enB5z73ObS2tqKmpgYLFy7E3r17sXPnTng8HvzlL39h2tYRdrDvk4yTTz4ZoVAIzzzzDGbNmpX3GnifTGwH+x5Zs2YN/vu//xsjIyOoqKhQztm8eTNisRjOPPNM3H777ZpGh7xHJr6DfZ90dXXhE5/4BAKBAGbOnIkZM2agqakJe/bsgdfrxZ/+9CcsXLhQcw7vk4ljaGgIZ5xxBkZGRjB16tScPyvRxz/+ceUJ4B/+8AfceuutMJvNWLx4MYqKivDGG29gZGQEF198MW688UbNualUCl/5ylfw8ssvw+VyYcmSJQiFQli3bh1kWcZNN92U01CX98nEcbjuEz2ZP+O1a9fqZkbyPpkYDsc98utf/xq///3vAQDnnHMObDab7ufb7Xbl4SgwMe4RBgrogOT7pQ1IzxS+66678Oyzz6K7uxvl5eVYtGgRrrzyShx33HG6n5lMJrFy5UqsXLkSO3fuhCRJmDlzJj7zmc/goosu0j1nYGAAd955J/7zn/+gp6cHFRUVWLx4Mb72ta8Z1v3Q4XMo7pN4PI4FCxYASKeSu1yugtfB+2TiOhT3SEtLC/74xz9i9erV6Onpgd1ux+zZs3HhhRfiwgsv1G10yHtkYjsU90lrayt+85vf4LXXXkMoFEJNTQ1OP/10XHnllaipqdE9h/fJxPD888/jv//7v8d17Le//W1ceeWVyusXX3wR9957L7Zs2QJJkjBt2jQsX74cn/zkJ3X/bojH4/jLX/6Cxx9/HM3NzXC5XJg7dy6uuuoqLF68WPc7eZ9MDIfzPslWKFAA8D6ZCA7HPXL++edjx44dBT+/uLhYCXBnHOl7hIECIiIiIiIiIlKwRwERERERERERKRgoICIiIiIiIiIFAwVEREREREREpGCggIiIiIiIiIgUDBQQERERERERkYKBAiIiIiIiIiJSMFBARERERERERAoGCoiIiIiIiIhIYTnSF0BERESHX1tbG84++2zd98xmM+x2O/x+P+bNm4fzzz8fZ5111mG+Qq3rrrsOf//733Xfs9vtcDqdqK+vx/vf/35cfPHFqKysPCTX0dTUhFmzZh2SzyYiIpooGCggIiI6xjU2NsLlcimvU6kUQqEQWltb0dzcjGeffRann346brvtNrjd7iN4pYDL5UJjY6NmXzQaRU9PDzZu3IiNGzfiz3/+M/73f/8X55577kH73p6eHtx8881Yt24dXnnllYP2uURERBMRAwVERETHuOuvvx5LlizJ2R+LxfDkk0/i5z//OVavXo2vfvWr+NOf/gSbzXYErjJt3rx5eOCBB3Tf27NnD37+859j1apVuPbaa+FwOHDmmWcelO9dvXo1nnnmmUOWqUBERDSRsEcBERER6bLZbLj44otx9913w2w2480338R99913pC/L0PTp0/H73/8e73vf+5BIJPC9730Pw8PDR/qyiIiIJh0GCoiIiCivU045BZ/5zGcAAH/84x8xOjp6hK/ImNlsxk9/+lNYrVYMDAzgwQcfPNKXRERENOmw9ICIiIgK+sxnPoMHH3wQgUAA69atw+mnn668Fw6Hcd999+H5559Hc3MzZFlGfX09PvShD+ELX/gCPB6P7mf29vbiwQcfxAsvvIC2tjbIsoxp06bhvPPOw2WXXXbAJQ6VlZU444wz8Pzzz+PFF1/EF7/4Rc37o6OjWLlyJV544QXs2LEDwWAQNpsNNTU1OP3007FixQpNicHs2bOV7e7ubuX1jh07NJ/7wgsv4JFHHsHmzZsRCoVQUlKCxYsX44orrsD8+fMP6GchIiI6EhgoICIiooLmzJkDl8uFcDiMN998UwkU7N69G1/84hfR3t4Os9mM+vp6FBUVYdeuXfjd736HJ554Avfccw9mzJih+by3334bX//619Hf3w+LxYIZM2YgFoth27Zt2Lp1K1555ZX31A/hpJNOwvPPP4/NmzcjEonA4XAAAAYGBnD55Zdj586dkCQJU6ZMQXV1Nbq7u9HU1ISmpiY89dRTePzxx1FVVQUAOPHEEzEwMIB9+/bBarVi4cKFmu9KJBK47rrr8PTTTwMAysrKMHv2bLS1teGZZ57Bs88+i+9973u49NJLD+hnISIiOtxYekBERETjUltbCwDo6OgAAIyMjOArX/kK2tvbcfbZZ+Oll17Cv/71Lzz55JN4+eWXccYZZ6C9vR1f/epXNeUKQ0ND+OY3v4n+/n68//3vx8svv4ynnnoKzz33HB599FGUlpbizTffxO9+97sDvta6ujoA6UV8T0+Psv8Xv/gFdu7ciYaGBjz33HP497//jZUrV2L16tX44x//CIfDgf7+fk0vhoceeghXXXUVAKC0tBQPPfQQHnroIeX93/zmN3j66adRVVWFP/7xj3jttdewcuVKvPbaa7j++ushSRJuvPFGrFmz5oB/HiIiosOJgQIiIiIaF6fTCQAIBAIAgEcffRTNzc2YP38+br/9dk26vt/vx29+8xvU1tZi3759ePzxx5X3Hn74YfT29qK2tha33347/H6/8t7ChQvxve99DwDw97//HalU6j1dq3i9iUQC69atgyRJ+O53v4upU6dqznnf+96njFTcuXPnuL6nv78f9957LwDgzjvvxPve9z7lPbPZjM9//vP4whe+AFmWcdtttx3Qz0JERHS4MVBARERE4xKPxwEAkiQBSNfkA8C5554Ls9mcc3xRURHOOeccAMBLL72k7M9sf+ITn0BRUVHOeeeccw6eeOIJ/Otf/4LJdGC/qmSuVbxei8WCF154ARs3bsQZZ5yRc44syyguLgaAcTdsfOWVVxCLxTBz5kzDPgSf+MQnAACbNm1Cf3///vwYRERERwR7FBAREdG4hEIhAFCaE2aeuj/66KN48cUXdc/p6+sDAOzZs0fZ19LSAiDd90CPzWbD3LlzD8q1itebYbfb0d/fjw0bNmDfvn1oa2vDnj17sG3bNgwNDQHAuDMZmpqaAABdXV1Yvny57jGyLCvbe/bsQVlZ2X79LERERIcbAwVERERUUCwWQ3t7OwAojQnD4TAAYN++fdi3b1/e88WFe6YUIPP0/lDYvXs3gHRQINNbAUhPWrj55pvx3HPPabIOHA4HFi5ciGQyibfffnvc35P5ucLhMN55552CxweDwXF/NhER0ZHCQAEREREVtGnTJmVhfeKJJwJIL65DoRB+//vf48wzzxz3Z2XOGx4ePiTXCkBZtC9atAhWqxUAEI1Gcfnll2P37t3w+XxYvnw5FixYgBkzZmDKlCkwm8349a9/vV+Bgsw0hXPOOQe//e1vD/4PQkREdASwRwEREREV9OijjwIAKioqcMoppwAApk2bBkBNv9ezb98+bN68GQMDA8q+TBNBo/Pi8TiWL1+Ob3zjG2htbd3va21pacEbb7wBADjvvPOU/S+88AJ2794Ni8WChx9+GFdffTU++MEPYtq0aUqPha6urv36rvH8N4hEInjzzTfR2tqKZDK5vz8OERHRYcdAAREREeX15ptv4qmnngIAfOlLX1IW1Zksgscee0y3+V8ikcBXv/pVfOpTn8LNN9+s7P/ABz4AAHj66acRi8Vyzlu1ahXeeecdvPrqq/tdz59KpfCjH/0IsiyjqqoKF1xwgfJeW1sbgPREhOyJB0C6n8LLL78MADkL+kxTRbHfQOZnMZvN2LNnj+H4w3vvvRef//zn8YlPfAKRSGS/fh4iIqIjgYECIiIi0jU8PIy//vWvuOqqq5BKpbB06VJNw75LLrkEfr8fzc3N+MpXvoKOjg7lvYGBAVx99dXYvXs3rFYrrrjiCuW9z33uc/D5fGhubsY111yj9CwA0iUON9xwAwBg+fLl+9XHYOvWrfjiF7+INWvWwGKx4Gc/+5lSGgAA06dPBwAMDQ3hvvvu0yz6N2zYgBUrVijXkr2gz1xHMBhUejMAQG1tLS6++GIAwP/8z//gP//5j/JeKpXCo48+ijvuuEP57+Vyucb98xARER0pkpwdGiciIqKjXltbG84++2wAQGNjo2YBm0gkEAwGNanyZ511Fn75y1/mLHQ3b96Mr3zlK+jt7YXJZMLMmTMhSRL27t2LWCwGi8WCX//61/jwhz+sOe+tt97CV7/6VQSDQdhsNsyaNQvBYBBtbW2QZRmnn3467rzzTtjtdgDAddddh7///e9wuVxobGxUPkeWZUQiEXR1dSmLfLfbjZtvvln5+TKSySQuueQSrF+/HgDg9/tRWVmJ3t5edHd3Q5IkLF26FK+99hp8Ph9ef/11ZbRic3MzPvKRjyCVSsHv96OiogJ/+tOfUFJSgmg0im9+85vK2MeKigpUVlaivb1dKbk455xz8Otf/1p3jCQREdFEw0ABERHRMUgMFGQzmUxwOByoqanBwoULcf7552Pp0qWGnzU4OIgHHngA//nPf9Dc3Ix4PA6/34/FixdjxYoVhmMQu7u78ac//QmvvPIKOjs7YTabMWvWLFx00UW4+OKLlXR/QA0U6LFarfD5fJgxYwbe97734VOf+hR8Pp/usaOjo3jggQfwj3/8A62trYjFYvD7/TjuuONwySWXYP78+ViyZAmi0SgefPBBnHTSScq5TzzxBO666y60t7fD7XbjnnvuwYIFCwCkAxbPPvssHn/8cbz77rsIhUJwOp2YM2cOLrzwQpx//vman4eIiGgiY6CAiIiIiIiIiBQMbRMRERERERGRgoECIiIiIiIiIlIwUEBERERERERECgYKiIiIiIiIiEjBQAERERERERERKRgoICIiIiIiIiIFAwVEREREREREpGCggIiIiIiIiIgUDBQQERERERERkYKBAiIiIiIiIiJSMFBARERERERERAoGCoiIiIiI/n87diwAAAAAMMjfet8oCiMAJgoAAACAiQIAAABgAXv9aOgWsccJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x='DecDate', y='Avg', data=co2_impute)\n",
    "plt.title(\"CO2 Average By Month, Imputed\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks pretty close to what we see on the NOAA [website](https://gml.noaa.gov/ccgg/trends/)!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Presenting the data: A Discussion on Data Granularity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the description:\n",
    "* monthly measurements are averages of average day measurements.\n",
    "* The NOAA GML website has datasets for daily/hourly measurements too.\n",
    "\n",
    "The data you present depends on your research question.\n",
    "\n",
    "**How do CO2 levels vary by season?**\n",
    "* You might want to keep average monthly data.\n",
    "\n",
    "**Are CO2 levels rising over the past 50+ years, consistent with global warming predictions?**\n",
    "* You might be happier with a **coarser granularity** of average year data!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "co2_year = co2_impute.groupby('Yr').mean()\n",
    "sns.lineplot(x='Yr', y='Avg', data=co2_year)\n",
    "plt.title(\"CO2 Average By Year\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Indeed, we see a rise by nearly 100 ppm of CO2 since Mauna Loa began recording in 1958."
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Tags",
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}